Centre for Research on Impact Evaluation (CRIE ... CS7792 Counterfactual Machine Learning , T. Joachims ... Counterfactual Evaluation of Treatment Assignment Functions with Networked Observational Data Ruocheng Guo Jundong Li y Huan Liu Abstract Counterfactual evaluation of novel treatment assignment functions (e.g., advertising algorithms and recommender sys-tems) is one of the most crucial causal inference problems for practitioners. Counterfactual Evaluation ENVIEVAL Jyrki Aakkula, Janne Artell & Heini Toikkanen MTT Agrifood Research Finland Grant Agreement Number 312071 Contents 1) Basic concept of counterfactual evaluation 2) Common Monitoring and Evaluation Framework (CMEF) and counterfactuals 3) Observations from the review of RDP evaluation reports As counterfactual examples become increasingly popular for explaining decisions of deep learning models, it is essential to understand what properties quantitative evaluation metrics do capture and equally important what they do not capture. To meet our two goals, we let through a fraction of transactions for review that we would otherwise block. SIGIR Tutorial on Counterfactual Evaluation and Learning for Search, Recommendation and Ad Placement, 2016. In M. Birnbaum & P. Mickwitz (Eds. In another ap- Counterfactual Learning - I. Counterfactual Learning - II. Counterfactual Evaluation and Learning. Counterfactual Evaluation Policy. In its simplest form, counterfactual impact evaluation (CIE) is a method of comparison which involves comparing the outcomes of interest of those having benefitted from a policy or programme (the "treated group") with those of a group similar in all respects to the treatment group (the "comparison/control . They produce train-ing data that significantly improve model general-ization, as well as contrast sets that help identify model vulnerabilities (Gardner et al.,2020), with around 70% less annotation e ort. Solving evaluation and training tasks using logged data is an exercise in counterfactual reasoning. The idea that counterfactual reasoning is central to rational agency has surfaced in another way in cognitive science and artificial intelligence, where encoding counterfactual-supporting relationships has emerged as a major theory of mental representation (Chater et al. Logical Counterfactual. In the first part, we will study scenarios where unknown . •The counterfactual represents how programme participants would have performed in the absence of the program •Problem: Counterfactual cannot be observed •Solution: We need to "mimic" or construct the counterfactual Different impact evaluation methodologies differ in how they construct the counterfactual Counterfactual The basic idea of counterfactual theories of causation is that the meaning of causal claims can be explained in terms of counterfactual conditionals of the form "If A had not occurred, C would not have occurred". The 'counterfactual' measures what would have happened to beneficiaries in the absence of the intervention, and impact is estimated by comparing counterfactual outcomes to those observed under the intervention. New Directions for Evaluation, 122, 75-84. One counterfactual might say to change feature A, the other counterfactual might say to leave A the same but change feature B, which is a contradiction. Counterfactual evaluation designs. Most counterfactual analyses have focused on claims of the form "event c caused event e", describing 'singular' or 'token' or 'actual' causation. Counterfactual thinking and impact evaluation in environmental. About The Centre for Research on Impact Evaluation (CRIE) is part of the Competence Centre on Microeconomic Evaluation (CC-ME).It provides scientific expertise and methodological support on Counterfactual Impact Evaluation (CIE) to the Directorate-General for Employment, Social Affairs and Inclusion (DG EMPL) and Member States, for impact evaluations of interventions funded through instruments . The method of counterfactual impact evaluation allows to identify which part of the observed actual improvement (e.g. One counterfactual might say to change feature A, the other counterfactual might say to leave A the same but change feature B, which is a contradiction. Personalizer is built on the assumption that users' behavior (and thus rewards) are impossible to predict retrospectively (Personalizer can't know what would have happened if the user had been shown something different than what they did see), and only to learn from . But utilizing a counterfactual survey may serve to illuminate changes that would otherwise go undetected. INTRODUCTION COUNTERFACTUAL FRAMEWORK IE DESIGNS & METHODS CASE STUDIES History, definition and justification What is a causal effect? This counterfactual world can be inferred only indirectly through evaluation designs that control for . Other sources for general background on machine learning are: Kevin Murphy, "Machine Learning - a Probabilistic Perspective", MIT Press, 2012. Counterfactual Evaluation and Learning Part 2 Adith Swaminathan, Thorsten Joachims Department of Computer Science & Department of Information Science Counterfactual impact evaluation. The counterfactual is an estimate of what would have happened in the absence of the program, and for suitable programs this can be a key element of the evaluation design. The challenge of IE Counterfactual Some people however argue that in turbulent, complex situations, it can be impossible to develop an accurate estimate of what would have happened in the absence of an intervention, since this absence would have affected the situation in ways that cannot be predicted. SIGIR Tutorial on Counterfactual Evaluation and Learning for Search, Recommendation and Ad Placement, 2016. counterfactual training and evaluation (§3), hu-mans label Polyjuice counterfactuals rather than creating them from scratch. They produce train-ing data that significantly improve model general-ization, as well as contrast sets that help identify model vulnerabilities (Gardner et al.,2020), with around 70% less annotation e ort. On Quantitative Evaluations of Counterfactuals. Going back to our fraud detection example, this would mean allowing a fraction of predicted fraudulent transactions to go through. In the counterfactual analysis, the outcomes of the intervention are compared with the outcomes that would have been achieved if the intervention had not been implemented. The slides for the tutorial are in four parts, and pdf's exported from Powerpoint are provided below. (2013) for counterfactual analysis. in the conditional distribution of Y given X. Counterfactual analysis consists of evaluating the e ects of such changes. Let's call this fraction P(allow). The basic idea of counterfactual theories of causation is that the meaning of causal claims can be explained in terms of counterfactual conditionals of the form "If A had not occurred, C would not have occurred". Video recording of the tutorial is in two parts, and embedded below. MIGUEL ANGEL LUQUE-FERNANDEZ A COUNTERFACTUAL APPROACH FOR IMPACT EVALUATION. In some cases, it is not possible to construct a counterfactual by creating a control group or a comparison group, but by constructing one logically. T. Joachims, A. Swaminathan. Video recording of the tutorial is in two parts, and embedded below. A counterfactual survey is only appropriate for attitudinal or perception data and not for objective measures of skill or knowledge. III. Personalizer is built on the assumption that users' behavior (and thus rewards) are impossible to predict retrospectively (Personalizer can't know what would have happened if the user had been shown something different than what they did see), and only to learn from . Going back to our fraud detection example, this would mean allowing a fraction of predicted fraudulent transactions to go through. This issue of multiple truths can be addressed either by reporting all counterfactual explanations or by having a criterion to evaluate counterfactuals and select the best one. The 'counterfactual' measures what would have happened to beneficiaries in the absence of the intervention, and impact is estimated by comparing counterfactual outcomes to those observed under the intervention. Counterfactual impact evaluation. So we will first review concepts from causal inference for counterfactual reasoning, assignment mechanisms, statistical estimation and learning theory. Let's call this fraction P(allow). ), Environmental program and policy evaluation. Counterfactual Evaluation - I. Counterfactual Evaluation - II. Offline Evaluations are done using a method called Counterfactual Evaluation. INTRODUCTION COUNTERFACTUAL FRAMEWORK IE DESIGNS & METHODS CASE STUDIES History, definition and justification What is a causal effect? Counterfactual analysis enables evaluators to attribute cause and effect between interventions and outcomes. In our specification, we consider a set of sensitive attribute values (e.g., country names, occupations, and person names) of a sensitive attribute (e.g., Country . The R package Counterfactual implements the methods of Cher-nozhukov et al. •System can be different from 0 that generated log. Evaluation: Outline • Evaluating Online Metrics Offline -A/B Testing (on-policy) Counterfactual estimation from logs (off-policy) • Approach 1: "Model the world" -Estimation via reward prediction In its simplest form, counterfactual impact evaluation (CIE) is a method of comparison which involves comparing the outcomes of interest of those having benefitted from a policy or programme (the "treated group") with those of a group similar in all respects to the treatment group (the "comparison/control . 3 Counterfactual Evaluation of Sentiment Bias Fairness specification. The last part emphasizes that counterfactual learning is a rich research area, and discuss several important research topics, such as optimization for counterfactual learning, counterfactual meta learning, stable learning, fairness, unbiased learning to rank, offline policy evaluation. Introduction to counterfactual evaluation approaches and how to use them in practice Stephen Morris, Professor of Evaluation, PERU, Manchester Metropolitan University: 09.30: Counterfactual approaches to causation Early on in the process of designing an evaluation you will need to consider how a counterfactualwill be identified and estimated. T. Joachims, A. Swaminathan. Some people however argue that in turbulent, complex situations, it can be impossible to develop an accurate estimate of what would have happened in the absence of an intervention, since this absence would have affected the situation in ways that cannot be predicted. About Press Copyright Contact us Creators Advertise Developers Terms Privacy Policy & Safety How YouTube works Test new features Press Copyright Contact us Creators . The counterfactual. The thesis then contains two parts. The science of impact evaluation was the subject of a two-week technical training workshop organized jointly by the Transfer Project and the African Economic Research Consortium . Most counterfactual analyses have focused on claims of the form "event c caused event e", describing 'singular' or 'token' or 'actual' causation.

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