Generic Machine Learning Inference on Heterogenous Treatment Effects in Randomized Experiments

By Victor Chernozhukov, Mert Demirer, Esther Duflo, Iv'an Fern'andez-Val. 2017

208 citations recorded by the source corpus.

Subfields: Statistics and Probability, Causal Inference & Discovery

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Citations in the corpus

Semiparametric Counterfactual Density Estimation (2021, background): “…Huber, 1967, Rakhlin et al., 2017, Rinaldo and Wasserman, 2010, Tsybakov, 2003, Wasserman, 2006, White, 1982, 1996] as well as in causal inference [Chernozhukov et al., 2018b, Cuellar and Kennedy, 2020, Kennedy et al., 2019, Neugebauer and van der Laan, 2007, Semenova and Chernozhukov, 2020, van…”
Can Microfinance Unlock a Poverty Trap for Some Entrepreneurs? (2019, background): “Moreover, in a study using data from a Moroccan microcredit RCT leveraging machine learning methods to detect heterogeneous treatment effects, Chernozhukov et al. (2018) use double machine learning and find that the existence of a prior business is the only household level variable that…”
Estimation and Inference about Heterogeneous Treatment Effects in High-Dimensional Dynamic Panels. (2017, background): “Inference on heterogeneous treatment effects is an important problem (see, e.g, Athey and Imbens (2016), Chernozhukov et al. (2017c)).”
Estimating Treatment Effects with Causal Forests: An Application (2019, unknown): “We also try a second test for heterogeneity, motivated by the “best linear predictor” method of Chernozhukov et al. (2018), that seeks to ﬁt the CATE as a linear function of the the out-of-bag causal forest estimates ˆ τ ( − i ) ( X i ).”
The Mortality and Medical Costs of Air Pollution: Evidence from Changes in Wind Direction (2019, unknown): “Second, we are the first to apply the Chernozhukov et al. (2018) method of generic machine learning inference on heterogeneous treatment effects to a quasi-experimental study design.”
How to Improve Tax Compliance? Evidence from Population-Wide Experiments in Belgium (2019, unknown): “25 According to Chernozhukov et al. (2018), this is a case in which the Wager and Athey (2018) method provides robust results: we have 10 dimensions of heterogeneity and about 230,000 observations (logð230,000Þ 5 12 > 10).”