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Journal articles on the topic 'Counterfactual evaluation'

Author: Grafiati

Published: 1 June 2024

Last updated: 31 July 2025

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1

Markman, Keith D., Matthew N. McMullen, Ronald A. Elizaga, and Nobuko Mizoguchi. "Counterfactual thinking and regulatory fit." Judgment and Decision Making 1, no. 2 (2006): 98–107. http://dx.doi.org/10.1017/s193029750000231x.

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AbstractAccording to regulatory fit theory (Higgins, 2000), when people make decisions with strategies that sustain their regulatory focus orientation, they “feel right” about what they are doing, and this “feeling-right” experience then transfers to subsequent choices, decisions, and evaluations. The present research was designed to link the concept of regulatory fit to functional accounts of counterfactual thinking. In the present study, participants generated counterfactuals about their anagram performance, after which persistence on a second set of anagrams was measured. Under promotion fr

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Bertolotti, Mauro, and Patrizia Catellani. "The Effects of Counterfactual Attacks on the Morality and Leadership of Different Professionals." Social Psychology 49, no. 3 (2018): 154–67. http://dx.doi.org/10.1027/1864-9335/a000338.

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Abstract. Past research has offered contrasting results regarding the effects of attacks on social judgments. In three experiments, we investigated the effects of counterfactual (“If only…”) and non-counterfactual attacks on the morality versus leadership of politicians versus entrepreneurs. First, participants rated morality as the most desirable, but least typical dimension of politicians, and leadership as the most desirable and most typical dimension of entrepreneurs (Study 1). Then, counterfactual attacks led to poorer evaluation of the attacked target and better evaluation of the attacki

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Hannikainen, Ivar. "Might-counterfactuals and the principle of conditional excluded middle." Disputatio 4, no. 30 (2011): 127–49. http://dx.doi.org/10.2478/disp-2011-0003.

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Abstract Owing to the problem of inescapable clashes, epistemic accounts of might-counterfactuals have recently gained traction. In a different vein, the might argument against conditional excluded middle has rendered the latter a contentious principle to incorporate into a logic for conditionals. The aim of this paper is to rescue both ontic mightcounterfactuals and conditional excluded middle from these disparate debates and show them to be compatible. I argue that the antecedent of a might-counterfactual is semantically underdetermined with respect to the counterfactual worlds it selects fo

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Buliga, Andrei, Chiara Di Francescomarino, Chiara Ghidini, Marco Montali, and Massimiliano Ronzani. "Generating Counterfactual Explanations Under Temporal Constraints." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 15 (2025): 15622–31. https://doi.org/10.1609/aaai.v39i15.33715.

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Counterfactual explanations are one of the prominent eXplainable Artificial Intelligence (XAI) techniques, and suggest changes to input data that could alter predictions, leading to more favourable outcomes. Existing counterfactual methods do not readily apply to temporal domains, such as that of process mining, where data take the form of traces of activities that must obey to temporal background knowledge expressing which dynamics are possible and which not. Specifically, counterfactuals generated off-the-shelf may violate the background knowledge, leading to inconsistent explanations. This

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Rylková, Žaneta, Karel Stelmach, and Petr Vlček. "Overall Equipment Effectiveness within Counterfactual Impact Evaluation Concept." Scientific Annals of Economics and Business 64, s1 (2017): 29–40. http://dx.doi.org/10.1515/saeb-2017-0037.

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Abstract Counterfactual impact evaluation (CIE) is a scientific quantitative approach mainly based on experiments and quasi experiments. CIE is trying to prove a causal relationship between outputs and outcomes. CIE does not take into account coherence of external incentives of companies with internal incentives that have or may have an impact on the behaviour of enterprises. The paper sets up internal evaluation indicators for businesses, counterfactuals useful for creating a more complex metrics evaluating businesses in the area of performance. The aim of the paper is to present model situat

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Schleich, Maximilian, Zixuan Geng, Yihong Zhang, and Dan Suciu. "GeCo." Proceedings of the VLDB Endowment 14, no. 9 (2021): 1681–93. http://dx.doi.org/10.14778/3461535.3461555.

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Machine learning is increasingly applied in high-stakes decision making that directly affect people's lives, and this leads to an increased demand for systems to explain their decisions. Explanations often take the form of counterfactuals , which consists of conveying to the end user what she/he needs to change in order to improve the outcome. Computing counterfactual explanations is challenging, because of the inherent tension between a rich semantics of the domain, and the need for real time response. In this paper we present CeCo, the first system that can compute plausible and feasible cou

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Wauters, Benedict, and Derek Beach. "Process tracing and congruence analysis to support theory-based impact evaluation." Evaluation 24, no. 3 (2018): 284–305. http://dx.doi.org/10.1177/1356389018786081.

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Theory-based impact evaluations have been put forward increasingly as an alternative for counterfactual impact evaluations. However, this raises questions regarding the foundations of drawing causal inference on the basis of such approaches. Case study methods such as QCA (Quantitative Comparative Analysis), process tracing and congruence analysis are emerging as a way to match the methodological rigor of counterfactuals. While QCA relies on multiple cases, process tracing and congruence analysis are methods that claim to be able to draw causal inference within a single case. In this article,

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Clark-Moorman, Kyleigh, Jason Rydberg, and Edmund F. McGarrell. "Impact Evaluation of a Parolee-Based Focused Deterrence Program on Community-Level Violence." Criminal Justice Policy Review 30, no. 9 (2018): 1408–30. http://dx.doi.org/10.1177/0887403418812999.

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We estimate the impact of a parolee-based focused deterrence (“pulling levers”) intervention on community-level firearm and non-firearm violence in Rockford, Illinois, via a retrospective, quasi-experimental design. Focusing on incidents of firearm violence in Rockford over a period of 60 months (38 months pre-intervention, 22 months post-intervention), program impact is assessed using Bayesian Structural Time Series (BSTS) models, constructing a synthetic control-based counterfactual time series from National Incident-Based Reporting System (NIBRS) data from 59 non-treated cities of similar s

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Castaño, Javier, Maria Blanco, and Pilar Martinez. "Reviewing Counterfactual Analyses to Assess Impacts of EU Rural Development Programmes: What Lessons Can Be Learned from the 2007–2013 Ex-Post Evaluations?" Sustainability 11, no. 4 (2019): 1105. http://dx.doi.org/10.3390/su11041105.

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Counterfactual analysis has been recommended as a means of assessing the impacts of European Rural Development Programmes (RDP) over recent years, although its application has been scarce to date. This paper examines the use of counterfactual analysis to assess socioeconomic impacts in a set of 2007–2013 ex-post evaluations. The analysis undertaken shows that a wide variety of counterfactual approaches have been applied, although certain barriers still remain to address the estimation of RDP impacts following the EU evaluation standards. Furthermore, we noted that impacts provided by individua

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Domnich, Marharyta, Julius Välja, Rasmus Moorits Veski, et al. "Towards Unifying Evaluation of Counterfactual Explanations: Leveraging Large Language Models for Human-Centric Assessments." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 15 (2025): 16308–16. https://doi.org/10.1609/aaai.v39i15.33791.

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As machine learning models evolve, maintaining transparency demands more human-centric explainable AI techniques. Counterfactual explanations, with roots in human reasoning, identify the minimal input changes needed to obtain a given output and, hence, are crucial for supporting decision-making. Despite their importance, the evaluation of these explanations often lacks grounding in user studies and remains fragmented, with existing metrics not fully capturing human perspectives. To address this challenge, we developed a diverse set of 30 counterfactual scenarios and collected ratings across 8

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Zhang, Qiyuan, and Judith Covey. "Past and Future Implications of Near-Misses and Their Emotional Consequences." Experimental Psychology 61, no. 2 (2014): 118–26. http://dx.doi.org/10.1027/1618-3169/a000231.

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The Reflection and Evaluation Model (REM) of comparative thinking predicts that temporal perspective could moderate people’s emotional reactions to close counterfactuals following near-misses ( Markman & McMullen, 2003 ). The experiments reported in this paper tested predictions derived from this theory by examining how people’s emotional reactions to a near-miss at goal during a football match (Experiment 1) or a close score in a TV game show (Experiment 2) depended on the level of perceived future possibility. In support of the theory it was found that the presence of future possibility

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Snider, Todd, and Adam Bjorndahl. "Informative counterfactuals." Semantics and Linguistic Theory 25 (October 29, 2015): 1. http://dx.doi.org/10.3765/salt.v25i0.3077.

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A single counterfactual conditional can have a multitude of interpretations that differ, intuitively, in the connection between antecedent and consequent. Using structural equation models (SEMs) to represent event dependencies, we illustrate various types of explanation compatible with a given counterfactual. We then formalize in the SEM framework the notion of an acceptable explanation, identifying the class of event dependencies compatible with a given counterfactual. Finally, by incorporating SEMs into possible worlds, we provide an update semantics with the enriched structure necessary for

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Markman, Keith D., and Matthew N. McMullen. "Counterfactuals need not be comparative: The case of “As if”." Behavioral and Brain Sciences 30, no. 5-6 (2007): 461–62. http://dx.doi.org/10.1017/s0140525x07002671.

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AbstractByrne (2005) assumes that counterfactual thinking requires a comparison of facts with an imagined alternative. In our view, however, this assumption is unnecessarily restrictive. We argue that individuals do not necessarily engage in counterfactual simulations exclusively to evaluate factual reality. Instead, comparative evaluation is often suspended in favor of experiencing the counterfactual simulation as if it were real.

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Ross, Stephen L., Eric Brunner, and Rachel Rosen. "Identification and Counterfactuals for Program Evaluation of Career and Technical Education." Career and Technical Education Research 46, no. 3 (2021): 15–36. http://dx.doi.org/10.5328/cter46.3.15.

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This paper considers recent efforts to conduct experimental and quasi-experimental evaluations of career and technical education programs. It focuses on understanding the counterfactual, or control population, for these program evaluations, discussing how the educational experiences of the control population might vary from those of the treated population and the ways in which the treatment and control populations used for evaluation may differ from each other. The paper begins by discussing the key identification strategies and the associated assumptions used to identify program effects, incl

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Du, Zaichao, and Pei Pei. "A simple and robust counterfactual impact evaluation." Economics Letters 207 (October 2021): 110015. http://dx.doi.org/10.1016/j.econlet.2021.110015.

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Cummings, Rick. "‘What if’: The counterfactual in program evaluation." Evaluation Journal of Australasia 6, no. 2 (2006): 6–15. http://dx.doi.org/10.1177/1035719x0600600203.

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de Oliveira, Raphael Mazzine Barbosa, and David Martens. "A Framework and Benchmarking Study for Counterfactual Generating Methods on Tabular Data." Applied Sciences 11, no. 16 (2021): 7274. http://dx.doi.org/10.3390/app11167274.

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Counterfactual explanations are viewed as an effective way to explain machine learning predictions. This interest is reflected by a relatively young literature with already dozens of algorithms aiming to generate such explanations. These algorithms are focused on finding how features can be modified to change the output classification. However, this rather general objective can be achieved in different ways, which brings about the need for a methodology to test and benchmark these algorithms. The contributions of this work are manifold: First, a large benchmarking study of 10 algorithmic appro

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Gabriel, Erin E., and Dean Follmann. "Augmented trial designs for evaluation of principal surrogates." Biostatistics 17, no. 3 (2016): 453–67. http://dx.doi.org/10.1093/biostatistics/kxv055.

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Abstract Observation of counterfactual intermediate responses, and evaluation of them as candidate surrogates, is complicated in a standard randomized trial as half of the responses are systematically missing by design. Although some augmentation procedures exist for obtaining counterfactual responses, they are specific to vaccine trials. We outline extensions to the existing augmentations and suggest augmentations of three trial designs outside the setting of vaccines. We outline the assumptions needed to identify the causal estimands of interest under each augmented design, under which stand

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Mellow, David. "Counterfactuals and the Proportionality Criterion." Ethics & International Affairs 20, no. 4 (2006): 439–54. http://dx.doi.org/10.1111/j.1747-7093.2006.00044.x.

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It is widely held that, in order for a resort to war or military force to be morally justified, it must, in addition to having a cause that is just, be proportionate. In this essay I argue for the need to use a counterfactual baseline when making the proportionality evaluation. Specifically, I argue that the relevant counterfactual baseline must contain a moral qualifier. In defending my proposal, I also contend that the relevant goods and harms that are weighed in the proportionality evaluation are not as open-ended as is sometimes presumed.

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Ferraro, Paul J. "Counterfactual thinking and impact evaluation in environmental policy." New Directions for Evaluation 2009, no. 122 (2009): 75–84. http://dx.doi.org/10.1002/ev.297.

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Lobachevsky, Yakov P., Alexander V. Shemyakin, Nikolay V. Limarenko, Ivan A. Uspensky, and Ivan A. Yukhin. "Counterfactual Analysis of the Efficiency of Decontamination of Livestock Production Organic Wastes." Engineering Technologies and Systems 4, no. 33 (2023): 466–89. http://dx.doi.org/10.15507/2658-4123.033.202304.466-489.

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Introduction. The implementation of the decree of the President of the Russian Federation is aimed at ensuring the food security of the country and requires the industrialization of the agro-industrial sector. The effectiveness of industrialization depends on the use of automated, intelligent solutions at all stages of implementing technological processes. Livestock is an agro-industrial sector generating the largest amount of organic waste materials, which are potential energy carriers: litter, liquid manure, process effluents, etc. According to the data from the Russian Statistics Committee

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Chen, Jiangjie, Chun Gan, Sijie Cheng, Hao Zhou, Yanghua Xiao, and Lei Li. "Unsupervised Editing for Counterfactual Stories." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 10 (2022): 10473–81. http://dx.doi.org/10.1609/aaai.v36i10.21290.

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Creating what-if stories requires reasoning about prior statements and possible outcomes of the changed conditions. One can easily generate coherent endings under new conditions, but it would be challenging for current systems to do it with minimal changes to the original story. Therefore, one major challenge is the trade-off between generating a logical story and rewriting with minimal-edits. In this paper, we propose EDUCAT, an editing-based unsupervised approach for counterfactual story rewriting. EDUCAT includes a target position detection strategy based on estimating causal effects of the

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Pasupuleti, Murali Krishna. "Bias and Fairness in Large Language Models: Evaluation and Mitigation Techniques." International Journal of Academic and Industrial Research Innovations(IJAIRI) 05, no. 05 (2025): 442–51. https://doi.org/10.62311/nesx/rphcr6.

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Abstract: Large Language Models (LLMs) such as GPT, BERT, and LLaMA have transformed natural language processing, yet they exhibit social biases that can reinforce unfair outcomes. This paper systematically evaluates bias and fairness in LLMs across gender, race, and socioeconomic dimensions using benchmark datasets and fairness metrics. We assess bias through template-based probing, stereotype score measurement, and downstream task performance. We implement mitigation strategies including adversarial training, counterfactual data augmentation, and fairness-aware loss functions. Regression and

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Guo, Zhimeng, Zongyu Wu, Teng Xiao, Charu Aggarwal, Hui Liu, and Suhang Wang. "Counterfactual Learning on Graphs: A Survey." Machine Intelligence Research 22, no. 1 (2025): 17–59. https://doi.org/10.1007/s11633-024-1519-z.

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Abstract Graph-structured data are pervasive in the real-world such as social networks, molecular graphs and transaction networks. Graph neural networks (GNNs) have achieved great success in representation learning on graphs, facilitating various downstream tasks. However, GNNs have several drawbacks such as lacking interpretability, can easily inherit the bias of data and cannot model casual relations. Recently, counterfactual learning on graphs has shown promising results in alleviating these drawbacks. Various approaches have been proposed for counterfactual fairness, explainability, link p

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Huang, Wen, Lu Zhang, and Xintao Wu. "Achieving Counterfactual Fairness for Causal Bandit." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 6 (2022): 6952–59. http://dx.doi.org/10.1609/aaai.v36i6.20653.

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In online recommendation, customers arrive in a sequential and stochastic manner from an underlying distribution and the online decision model recommends a chosen item for each arriving individual based on some strategy. We study how to recommend an item at each step to maximize the expected reward while achieving user-side fairness for customers, i.e., customers who share similar profiles will receive a similar reward regardless of their sensitive attributes and items being recommended. By incorporating causal inference into bandits and adopting soft intervention to model the arm selection st

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Rowe, Andy. "Rapid impact evaluation." Evaluation 25, no. 4 (2019): 496–513. http://dx.doi.org/10.1177/1356389019870213.

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Rapid Impact Evaluation offers the potential to evaluate impacts in both ex ante and ex post settings, providing utility for developmental and formative evaluation as well as the usual summative settings. Rapid Impact Evaluation triangulates judgments of three separate groups of experts to assess the incremental change in effects attributable to the program. Three methodological innovations are central to the method: the scenario-based counterfactual, a simplified approach to measuring change in effects, and an interest-based approach to stakeholder engagement. In evaluations to date, Rapid Im

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Ashwani, Swagata, Kshiteesh Hegde, Nishith Reddy Mannuru, et al. "Cause and Effect: Can Large Language Models Truly Understand Causality?" Proceedings of the AAAI Symposium Series 4, no. 1 (2024): 2–9. http://dx.doi.org/10.1609/aaaiss.v4i1.31764.

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With the rise of Large Language Models (LLMs), it has become crucial to understand their capabilities and limitations in deciphering and explaining the complex web of causal relationships that language entails. Current methods use either explicit or implicit causal reasoning, yet there is a strong need for a unified approach combining both to tackle a wide array of causal relationships more effectively. This research proposes a novel architecture called Context-Aware Reasoning Enhancement with Counterfactual Analysis (CARE-CA) to enhance causal reasoning and explainability. The proposed framew

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Keogh, Ruth H., and Nan Van Geloven. "Prediction Under Interventions: Evaluation of Counterfactual Performance Using Longitudinal Observational Data." Epidemiology 35, no. 3 (2024): 329–39. http://dx.doi.org/10.1097/ede.0000000000001713.

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Predictions under interventions are estimates of what a person’s risk of an outcome would be if they were to follow a particular treatment strategy, given their individual characteristics. Such predictions can give important input to medical decision-making. However, evaluating the predictive performance of interventional predictions is challenging. Standard ways of evaluating predictive performance do not apply when using observational data, because prediction under interventions involves obtaining predictions of the outcome under conditions that are different from those that are observed for

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Yuan, Yuyu, Pengqian Zhao, Ting Guo, and Hongpu Jiang. "Counterfactual-Based Action Evaluation Algorithm in Multi-Agent Reinforcement Learning." Applied Sciences 12, no. 7 (2022): 3439. http://dx.doi.org/10.3390/app12073439.

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Multi-agent reinforcement learning (MARL) algorithms have made great achievements in various scenarios, but there are still many problems in solving sequential social dilemmas (SSDs). In SSDs, the agent’s actions not only change the instantaneous state of the environment but also affect the latent state which will, in turn, affect all agents. However, most of the current reinforcement learning algorithms focus on analyzing the value of instantaneous environment state while ignoring the study of the latent state, which is very important for establishing cooperation. Therefore, we propose a nove

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Jacobone, Vittoria, Giuseppe Moro, and Caterina Balenzano. "Counterfactual evaluation of a public programme for youth-led projects." RIV Rassegna Italiana di Valutazione, no. 75 (June 2021): 87–115. http://dx.doi.org/10.3280/riv2019-075006.

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Pseiridis, Anastasia, and Ioannis Kostopoulos. "A Counterfactual Impact Evaluation of EU State Aid in Greece." WSEAS TRANSACTIONS ON BUSINESS AND ECONOMICS 20 (January 17, 2023): 352–72. http://dx.doi.org/10.37394/23207.2023.20.33.

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EU state aid adopted from Member States is increasing at a fast pace due to the Covid-19 pandemic and energy crisis. Given its impact on the European economy, securing a maximum value added is a challenge for both policy makers and public administration. State aid impact depends not only on available resources but also on spending decisions that must be in line with state aid rules. It is believed that new policies would benefit if they were based on assessed evidence of existing policies during periods with similar characteristics. Our contribution analyses the characteristics of Greek develo

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Narita, Yusuke, Kyohei Okumura, Akihiro Shimizu, and Kohei Yata. "Counterfactual Learning with General Data-Generating Policies." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 8 (2023): 9286–93. http://dx.doi.org/10.1609/aaai.v37i8.26113.

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Off-policy evaluation (OPE) attempts to predict the performance of counterfactual policies using log data from a different policy. We extend its applicability by developing an OPE method for a class of both full support and deficient support logging policies in contextual-bandit settings. This class includes deterministic bandit (such as Upper Confidence Bound) as well as deterministic decision-making based on supervised and unsupervised learning. We prove that our method's prediction converges in probability to the true performance of a counterfactual policy as the sample size increases. We v

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Altmeyer, Patrick, Mojtaba Farmanbar, Arie Van Deursen, and Cynthia C. S. Liem. "Faithful Model Explanations through Energy-Constrained Conformal Counterfactuals." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 10 (2024): 10829–37. http://dx.doi.org/10.1609/aaai.v38i10.28956.

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Counterfactual explanations offer an intuitive and straightforward way to explain black-box models and offer algorithmic recourse to individuals. To address the need for plausible explanations, existing work has primarily relied on surrogate models to learn how the input data is distributed. This effectively reallocates the task of learning realistic explanations for the data from the model itself to the surrogate. Consequently, the generated explanations may seem plausible to humans but need not necessarily describe the behaviour of the black-box model faithfully. We formalise this notion of

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Admassu, Tsehay. "Evaluation of Local Interpretable Model-Agnostic Explanation and Shapley Additive Explanation for Chronic Heart Disease Detection." Proceedings of Engineering and Technology Innovation 23 (January 1, 2023): 48–59. http://dx.doi.org/10.46604/peti.2023.10101.

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This study aims to investigate the effectiveness of local interpretable model-agnostic explanation (LIME) and Shapley additive explanation (SHAP) approaches for chronic heart disease detection. The efficiency of LIME and SHAP are evaluated by analyzing the diagnostic results of the XGBoost model and the stability and quality of counterfactual explanations. Firstly, 1025 heart disease samples are collected from the University of California Irvine. Then, the performance of LIME and SHAP is compared by using the XGBoost model with various measures, such as consistency and proximity. Finally, Pyth

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Kopečná, Vědunka. "Counterfactual Impact Evaluation of the Project Internships for Young Job Seekers." Central European Journal of Public Policy 10, no. 2 (2016): 48–66. http://dx.doi.org/10.1515/cejpp-2016-0026.

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Abstract The growing youth unemployment across Europe raises the need to take appropriate measures. One of steps taken towards decreasing it by the European Union has been the program Youth Guarantee, implemented by a number of member states. Despite the relatively lower youth unemployment, the Czech Republic has implemented this program as well, and supported the realization of the project Internships for Young Job Seekers, whose aim was to ease the transition for students from schools to the labour market thanks to internships in companies. The effects which internship related project bring

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Lopez Buenache, German. "Monetary policy evaluation. A counterfactual analysis based on dynamic factor models." Applied Economics Letters 24, no. 7 (2016): 460–66. http://dx.doi.org/10.1080/13504851.2016.1203053.

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Cirkovic, Milan. "Counterfactuals and unphysical ceteris paribus: An explanatory fallacy." Filozofija i drustvo 24, no. 4 (2013): 143–60. http://dx.doi.org/10.2298/fid1304143c.

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I reconsider a type of counterfactual argument often used in historical sciences on a recent widely discussed example of the so-called ?rare Earth? hypothesis in planetary sciences and astrobiology. The argument is based on the alleged ?rarity? of some crucial ingredient for the planetary habitability, which is, in Earth?s case, provided by contingent evolutionary development. For instance, the claim that a contingent fact of history which has created planet Jupiter enables shielding of Earth from most dangerous impact catastrophes, thus increasing Earth?s habitability, leads often to the conc

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Xiao, Ying, Jie M. Zhang, Yepang Liu, Mohammad Reza Mousavi, Sicen Liu, and Dingyuan Xue. "MirrorFair: Fixing Fairness Bugs in Machine Learning Software via Counterfactual Predictions." Proceedings of the ACM on Software Engineering 1, FSE (2024): 2121–43. http://dx.doi.org/10.1145/3660801.

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With the increasing utilization of Machine Learning (ML) software in critical domains such as employee hiring, college admission, and credit evaluation, ensuring fairness in the decision-making processes of underlying models has emerged as a paramount ethical concern. Nonetheless, existing methods for rectifying fairness issues can hardly strike a consistent trade-off between performance and fairness across diverse tasks and algorithms. Informed by the principles of counterfactual inference, this paper introduces MirrorFair, an innovative adaptive ensemble approach designed to mitigate fairnes

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Oosterhuis, Harrie. "Learning from user interactions with rankings." ACM SIGIR Forum 54, no. 2 (2020): 1–2. http://dx.doi.org/10.1145/3483382.3483402.

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Ranking systems form the basis for online search engines and recommendation services. They process large collections of items, for instance web pages or e-commerce products, and present the user with a small ordered selection. The goal of a ranking system is to help a user find the items they are looking for with the least amount of effort. Thus the rankings they produce should place the most relevant or preferred items at the top of the ranking. Learning to rank is a field within machine learning that covers methods which optimize ranking systems w.r.t. this goal. Traditional supervised learn

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Murray, Aja Louise, Helen Wright, Hannah Casey, et al. "Introducing DigiCAT: A digital tool to promote the principled use of counterfactual analysis for identifying potential active ingredients in mental health." Wellcome Open Research 9 (July 16, 2024): 376. http://dx.doi.org/10.12688/wellcomeopenres.21105.1.

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Background Given the challenges and resources involved in mental health intervention development and evaluation, it is valuable to obtain early evidence on which intervention targets represent the most promising investments. Observational datasets provide a rich resource for exploring these types of questions; however, the lack of randomisation to treatments in these data means they are vulnerable to confounding issues. Counterfactual analysis refers to a family of techniques within the potential outcomes framework that can help address confounding. In doing so, they can help differentiate pot

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Camacho Murillo, Andres, and Natalia Arango Ramírez. "Transforming Lives: The Positive Impact of School Retention Strategies on the Probability of Students’ Dropout in Medellin." Revista de Economía del Rosario 26, no. 2 (2025): 1–34. https://doi.org/10.12804/revistas.urosario.edu.co/economia/a.14801.

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This study assesses the causal effect of school retention strategies on the probability of school dropout in Medellin, Colombia. The probit model is estimated using microdata on enrollment published by the Ministry of National Education and data on beneficiaries of school retention programs, year 2019. Three impact evaluation methods are employed to obtain the counterfactual group of each school retention program: Self- Selected Comparisons, Propensity Score Matching, and Endogenous Treatment-Effects. Results from the latter method show that the probability of school dropout is lower for stude

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Cunha-e-Sá, Maria A., Rita Freitas, Luis Catela Nunes, and Vladimir Otrachshenko. "On nature’s shoulders." Tourism Economics 24, no. 4 (2017): 369–85. http://dx.doi.org/10.1177/1354816617731195.

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The use of counterfactual methods in the evaluation of policy interventions has been accepted today as the best approach in the estimation of a program’s performance. However, the simplest evaluations are often quite demanding in terms of the resources and the time needed to be implemented. In this article, we study the economic impact of a tourism media campaign launched in Nazaré, an old fishing community on the west coast of Portugal, to make big waves visible to the world. The campaign provided the required “informational media infrastructure” that created the public awareness necessary to

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Kunimi, Takara, and Hajime Seya. "Identification of the geographical extent of an area benefiting from a transportation project: A generalized synthetic control." Journal of Transport and Land Use 14, no. 1 (2021): 25–45. http://dx.doi.org/10.5198/jtlu.2021.1784.

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In evaluating the benefits of an infrastructure project, it is essential to consider who is benefiting from the project and where benefits are located. However, there is no established way to accurately determine the latter. To fill this methodological gap, this study proposes an approach for the ex-post identification of the geographical extent of an area benefiting from a transportation project based on a generalized synthetic control method. Specifically, it allows comparing multiple treatment units with their counterfactuals in a single run—changes in land prices (actual outcome) at each t

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Benga, Elita, Juris Hāzners, and Zaiga Miķelsone. "DISPLACEMENT EFFECTS OF LATVIAN RURAL DEVELOPMENT PROGRAMME 2007-2013." Environment. Technology. Resources. Proceedings of the International Scientific and Practical Conference 1 (June 15, 2017): 28. http://dx.doi.org/10.17770/etr2017vol1.2662.

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Periodic evaluation of EU Member States Rural Development Programme (RDP) specific policy interventions is considered crucial in policy development. The main reasons for the evaluation of specific policy interventions are the assessment of a programme’s impact, the improvement of programme management and administration, identification of necessary improvements in the delivery of interventions and meeting the accountability. The core question to be answered in programme evaluation is whether the stated objectives are accomplished by particular intervention (support or „treatment” provided to pr

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D’Alberto, Riccardo, Matteo Zavalloni, Meri Raggi, and Davide Viaggi. "AES Impact Evaluation With Integrated Farm Data: Combining Statistical Matching and Propensity Score Matching." Sustainability 10, no. 11 (2018): 4320. http://dx.doi.org/10.3390/su10114320.

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A large share of the Common Agricultural Policy (CAP) is allocated to agri-environmental schemes (AESs), whose goal is to foster the provision of a wide range of environmental public goods. Despite this effort, little is known on the actual environmental and economic impact of the AESs, due to the non-experimental conditions of the assessment exercise and several data availability issues. The main objective of the paper is to explore the feasibility of combining the non-parametric statistical matching (SM) method and propensity score matching (PSM) counterfactual approach analysis and to test

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Sun, Yuewen, Erli Wang, Biwei Huang, et al. "ACAMDA: Improving Data Efficiency in Reinforcement Learning through Guided Counterfactual Data Augmentation." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 14 (2024): 15193–201. http://dx.doi.org/10.1609/aaai.v38i14.29442.

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Data augmentation plays a crucial role in improving the data efficiency of reinforcement learning (RL). However, the generation of high-quality augmented data remains a significant challenge. To overcome this, we introduce ACAMDA (Adversarial Causal Modeling for Data Augmentation), a novel framework that integrates two causality-based tasks: causal structure recovery and counterfactual estimation. The unique aspect of ACAMDA lies in its ability to recover temporal causal relationships from limited non-expert datasets. The identification of the sequential cause-and-effect allows the creation of

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Borgonovo, Claudia. "Modales ambiguos." Revue Romane / Langue et littérature. International Journal of Romance Languages and Literatures 46, no. 2 (2011): 202–21. http://dx.doi.org/10.1075/rro.46.2.02bor.

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This article explores the interaction of Tense, Aspect and Modality in French, Italian and Spanish, languages in which Modals are inflected as main verbs. Imperfective modals are a-averidical, as modals are expected to be, but when they appear in a perfective tense, unexpected entailments and implicatures appear. For example, the following example is three-way ambiguous in Spanish; the corresponding example is two way-ambiguous in Italian and French: P. may have won, could have won, managed to win the race The three readings, epistemic, counterfactual and implicative, are derived from the alte

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Tian, Meng, Tongping Li, Shuwang Yang, Yiwei Wang, and Shuke Fu. "The Impact of High-Speed Rail on the Service-Sector Agglomeration in China." Sustainability 11, no. 7 (2019): 2128. http://dx.doi.org/10.3390/su11072128.

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High-speed rail (HSR) can potentially influence various economic activities across space. Estimating the impacts of HSR on service-sector agglomeration (SSA) was proven to be difficult but meaningful. In this paper, prefectural-level data from 1998 to 2016 and a panel data program evaluation method are employed to evaluate the effect of the Wuhan–Guangzhou HSR (WGHSR) on the SSA along the route. In this way, we construct hypothetical counterfactuals for SSA index of the WGHSR cities in the absence of the HSR projects using the SSA index in selected non-HSR cities. By comparing the counterfactu

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Latour, Chiara, Franco Peracchi, and Giancarlo Spagnolo. "Assessing alternative indicators for Covid-19 policy evaluation, with a counterfactual for Sweden." PLOS ONE 17, no. 3 (2022): e0264769. http://dx.doi.org/10.1371/journal.pone.0264769.

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Using the synthetic control method, we construct counterfactuals for what would have happened if Sweden had imposed a lockdown during the first wave of the COVID-19 epidemic. We consider eight different indicators, including a novel one that we construct by adjusting recorded daily COVID-19 deaths to account for weakly excess mortality. Correcting for data problems and re-optimizing the synthetic control for each indicator, we find that a lockdown would have had sizable effects within one week. The much longer delay estimated by two previous studies focusing on the number of positives cases is

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Markman, Keith D., Matthew N. McMullen, and Ronald A. Elizaga. "Counterfactual thinking, persistence, and performance: A test of the Reflection and Evaluation Model." Journal of Experimental Social Psychology 44, no. 2 (2008): 421–28. http://dx.doi.org/10.1016/j.jesp.2007.01.001.

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