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Counterfactual sensitivity and robustness

WebReinforcement learning (RL) has become a highly successful framework for learning in Markov decision processes (MDP). Due to the adoption of RL in realistic and complex environments, solution robustness becomes an increasingly important aspect of RL deployment. Nevertheless, current RL algorithms struggle with robustness to … WebNov 23, 2011 · Counterfactual Sensitivity and Robustness. Article. Jan 2024; Timothy Christensen; Benjamin Connault; We propose a framework for analyzing the sensitivity of counterfactuals to parametric ...

Auditing fairness under unawareness through counterfactual …

WebMar 8, 2024 · We first show that the bounds on counterfactual means under the f-sensitivity model are optimal solutions to a new class of distributionally robust optimization (DRO) programs, whose dual forms are essentially risk minimization problems. We then construct point estimators for these bounds by applying a novel debiasing technique to … WebFeb 1, 2024 · Thus, the aim of sensitivity analysis is to quantify the robustness of causal inference based on simulated scenarios. Frank et al. (2024) provide detailed reviews and illustrative examples of two significant methods for sensitivity analysis: the Impact Threshold for a Confounding Variable (ITCV) based on omitted variables in the linear … lays numb and spicy https://corpoeagua.com

FASTER-CE: Fast, Sparse, Transparent, and Robust Counterfactual ...

WebCounterfactual explana-tions state what would have happened had the input variables been changed in certain ways. These explanations are particularly useful when you want to help the user understand how they can change ... Robustness as a general desideratum has long been advocated by both natural and social scientists [6, 19]. The idea of ... Webthat the robust classifier is less sensitive to spurious corre-lations. While these prior works show the potential of using human annotations to improve model robustness, collecting such annotations can be costly. In this paper, we propose to train a robust classifier with automatically generated counterfactual samples. Specifi- WebCounterfactual Sensitivity and Robustness∗ Timothy Christensen† Benjamin Connault‡ May 14, 2024 Abstract We propose a framework for characterizing the sensitivity of counterfactuals with respect to parametric assumptions about the distribution of latent variables in a class of structural models. katy tx calendar of events

[1904.00989v3] Counterfactual Sensitivity and Robustness

Category:Timothy Christensen

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Counterfactual sensitivity and robustness

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WebOverview. The term "Counterfactual" is defined by the Merriam-Webster Dictionary as contrary to the facts. A counterfactual thought occurs when a person modifies a factual prior event and then assesses the consequences of that change. A person may imagine how an outcome could have turned out differently, if the antecedents that led to that event … WebJan 1, 2024 · Download Citation Counterfactual Sensitivity and Robustness We propose a framework for analyzing the sensitivity of counterfactuals to parametric assumptions about the distribution of latent ...

Counterfactual sensitivity and robustness

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WebFeb 2, 2024 · In Robust Weighted Conformal Inference, we propose a generic robust weighted conformal procedure, which is applied to counterfactual prediction in Counterfactual Inference with Confounding. Suppose we are interested in Y (1) and ( X n + 1 , Y n + 1 (1)) is a test sample from the superpopulation (the results apply to other types … WebJan 23, 2006 · Summary. Clinical trials of micronutrient supplementation are aimed at reducing the risk of infant mortality by increasing birth weight. Because infant mortalit

WebThe recent paper by Christensen and Connault (2024) introduces robustness ideas to analyze the sensitivity of counterfactuals to parametric assumptions about the distribution of latent variables ... Web本站追踪在深度学习方面的最新论文成果，每日更新最前沿的人工智能科研成果。同时可以根据个人偏好，为你智能推荐感兴趣的论文。 并优化了论文阅读体验，可以像浏览网页一样阅读论文，减少繁琐步骤。并且可以在本网站上写论文笔记，方便日后查阅

WebApr 4, 2024 · Counterfactual Sensitivity and Robustness. Monday, April 4, 2024 - 4:30pm - Monday, April 4, 2024 - 6:00pm. Econometrics Seminar PCPSE 101 United States More on Timothy Christensen. Signup Sheet. Timothy Christensen NYU. Download Paper. College of Arts & Sciences; Graduate Division ... Webin the real world data via the counterfactual data, we obtain a model that does not disparately have high false positive rates on specific demographic groups, while being sensitive to the intent of the sentence. We achieve this improvement in robust-ness, while improving the sliced accuracy across multiple protected identity subgroups of the data.

WebCounterfactual Sensitivity and Robustness∗ Timothy Christensen† Benjamin Connault‡ May 25, 2024 Abstract We propose a framework for analyzing the sensitivity of counterfactu

WebFeb 3, 2024 · Which approach to use to examine robustness will depend on how many pre-periods you have: with only a small number of pre-intervention periods, the Rambachan and Roth approach of bounding seems most applicable for sensitivity analysis; when you have more periods you can consider fitting different pre-trends as in Bilinski and Hatfield. lay snack maker crosswordWebOct 12, 2024 · While several properties desired of high-quality counterfactuals have been identified in the literature, three crucial concerns: the speed of explanation generation, robustness/sensitivity and ... lays name chipsWebThese methods are also known as “robustness or sensitivity analyses”. A series of recent works (Frank [2000, Sociological Methods and Research 29: 147–194]; ... counterfactual data as in Rubin’s causal model (Rubin 1974) or from a population from which observed cases were not sampled. This framework enables researchers to lays nachosWebTheir approach is specific to counterfactual CCPs and to dynamic binary choice models. Our approach allows for a wider range of counterfactual (e.g., welfare), shape restrictions, and multinomial choice, in addition to performing sensitivity analyses.4 Finally, our work is complementary to the recent literature on local sensitivity—see, lays new flavors 2022WebSep 21, 2024 · Counterfactual Sensitivity and Robustness Timothy Christensen and Benjamin Connault Northwestern Interactions Workshop. Discussion comments by Doug Miller. September 21, 2024 ... no need to specify a narrow family to be robust within • Stay within a neighborhood (delta) of baseline • Conceptual approach: search over all possible ... katy tx car serviceWebCounterfactual Sensitivity and Robustness∗ Timothy Christensen† Benjamin Connault‡ May 25, 2024 Abstract We propose a framework for analyzing the sensitivity of counterfactuals to para-metric assumptions about the distribution of latent variables in structural models. katy tx average weatherWebJan 1, 2024 · Counterfactual Sensitivity and Robustness. We propose a framework for analyzing the sensitivity of counterfactuals to parametric assumptions about the distribution of latent variables in structural models. In particular, we derive bounds on counterfactuals as the distribution of latent variables spans nonparametric neighborhoods of a given ... lays nacho chips