Estimating Conditional Average Treatment Effects

By Jason Abrevaya, Yu‐Chin Hsu, Robert P. Lieli. 2014

210 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Statistics and Probability

Citations in the corpus

Algorithmic recourse under imperfect causal knowledge: a probabilistic approach (2020, background): “The key idea behind this approach resembles the notion of conditional average treatment effects (CATE) [1] (illustrated in Fig.”
Doubly robust uniform confidence band for the conditional average treatment effect function (2016, methodology): “Abrevaya, Hsu, and Lieli (2015) also consider this problem in their application.”
Learning to Personalize from Observational Data (2016, background): “1, [15] proposed estimates based on propensity-score weighting and kernel regression.”
Nonparametric estimation of causal heterogeneity under high-dimensional confounding (2019, methodology): “Conditional average treatment effects were investigated by Abrevaya et al. (2015) and Lee et al. (2016) who find that mother’s age is associated with increasingly negative effects of smoking.”
Estimation of Conditional Average Treatment Effects With High-Dimensional Data (2019, methodology): “Similarly to Abrevaya et al. (2015) and LOW, the resulting convergence rate of the CATE estimators is √ Nhd, where N is the sample size and d = dim(X1). In addition to the errors bounds, the full-sample estimator also requires controlling the complexity (entropy) of the function space in which the n”
Testing the Unconfoundedness Assumption via Inverse Probability Weighted Estimators of (L)ATT (2014, methodology): “For example, Ichimura and Linton (2005) used local polynomial regression and Abrevaya, Hsu, and Lieli (2013) used higher order kernel regression.”
Nonparametric Tests for Treatment Effect Heterogeneity With Duration Outcomes (2016, background): “Abrevaya et al. (2015) and Lee et al.”
Simultaneous inference for Best Linear Predictor of the Conditional Average Treatment Effect and other structural functions (2017, methodology): “Indeed, the solutions offered by current literature have either restricted the number of controls to be smaller than sample size (Abrevaya et al. (2015), Athey and Imbens (2015), Wager and Athey (2016)) or ignored rich heterogeneity (Belloni et al. (2014), Belloni et al. (2016), Chernozhukov et al. ”
Recursive Partitioning for Personalization using Observational Data (2016, background): “1, Abrevaya et al. (2015) propose estimates based on propensity-score weighting and kernel regression.”