Estimation and inference for distribution functions and quantile functions in treatment effect models

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Testing the Unconfoundedness Assumption via Inverse Probability Weighted Estimators of (L)ATT (2014, methodology): “Even though the true instrument propensity score does not depend on the covariates, one can achieve a substantial reduction in the standard error of the estimator by allowing q̂ to be a function of X, as suggested by Theorem 3 of Frölich and Melly (2013b). For example, when q̂(X) is linear, the stan”
Nonparametric Tests for Treatment Effect Heterogeneity With Duration Outcomes (2016, methodology): “(A.1) Similar assumptions have adopted by Hirano, Imbens, and Ridder (2003), Crump et al. (2008), Donald and Hsu (2014), among others.”
Difference-in-Differences with multiple time periods (2018, result): “Bierens (1982), Bierens and Ploberger (1997), Stute (1997), and Escanciano (2006b, 2008); for a recent overview, see González-Manteiga and Crujeiras (2013). However, in contrast with most specification testing proposals, our null hypothesis involves multiple conditional moment restrictions instead o”
Universal Off-Policy Evaluation (2021, background): “Outside RL, works in the econometrics and causal inference literature have also considered quantile treatments [29, 99] and inferences on counterfactual distributions [28, 20, 36], but these methods are not developed for sequential decisions and do not provide any high-confidence bounds with guarant”