Machine Learning Methods for Estimating Heterogeneous Causal Eects

By Susan Athey, Guido W. Imbens. 2015

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Citations in the corpus

Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI (2019, neutral): “Causality Domain experts, managers and executive board members, regulatory entities/agencies [35, 38, 39, 40, 41, 42, 43]”
The mythos of model interpretability (2016, background): “But the optimization objective for most supervised learning models is simply to minimize error.”
Causal Falling Rule Lists (2015, methodology): “Methods have used tree structures (Su, Kang, and Levine 2012; Su and Nickerson 2009; Athey and Imbens 2015; Beygelzimer and Langford 2009) to form such treatment effect subgroups.”
Data Science of the People , for the People , by the People : A Viewpoint on an Emerging Dichotomy (2015, background): “Typically in Type B applications, as long as a feature adds predictive value in generalization, regardless of whether it has any causal relationship with the outcome, it is included in a model [4].”
The relationship between trust in AI and trustworthy machine learning technologies (2019, background): “• Explainability Technologies: technologies focused on explaining and interpreting the outcome to the stakeholders (including end-users) in a humane manner [6, 60, 17, 41, 86, 33, 19, 42, 76, 62, 89, 69].”
A Nonparametric Bayesian Analysis of Heterogenous Treatment Effects in Digital Experimentation (2014, methodology): “Athey and Imbens (2015) studied various strategies for the use of CART-like algorithms in prediction of HTE.”
Adaptive Concentration of Regression Trees, with Application to Random Forests (2015, background): “, prioritizing treatment to patients in leaves with the largest estimated treatment effect [2, 41].”
Machine Learning for the Developing World (2018, background): “Such needs could motivate novel research in the growing subfield of ML and causality (Athey and Imbens 2015; Schölkopf et al. 2012).”
A Survey of Learning Causality with Data (2018, background): “Athey and Imbens [10] describe decision trees and ensemble machine learning models for learning causal effects.”
Using Causal Inference to Estimate What-if Outcomes for Targeting Treatments (2016, background): “In comparison, standard approaches for modeling heterogeneous treatment responses rely on defining subpopulations from the data and estimating the average effect for each subpopulation given the covariates [Foster et al., 2010, Imai et al., 2013, Tian et al., 2014, Athey and Imbens, 2015].”
The Welfare Effects of Nudges: A Case Study of Energy Use Social Comparisons (2019, methodology): “29One alternative approach would be to use a tree method akin to those in Athey and Imbens (2015), splitting the WTP data and energy use data at the same nodes.”
A Minimax Surrogate Loss Approach to Conditional Difference Estimation (2018, background): “Recently there has been a lot of work on subgroup identification [16, 18, 25] and other types of nonlinear predictors of causal effects [3, 19, 2, 36, 34].”