Balancing Out Regression Error: Efficient Treatment Effect Estimation without Smooth Propensities

By David A. Hirshberg, Stefan Wager. 2017

32 citations recorded by the source corpus.

Subfields: Statistics and Probability, Causal Inference & Discovery

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Citations in the corpus

Double/De-Biased Machine Learning of Global and Local Parameters Using Regularized Riesz Representers (2018, methodology): “They utilize the orthogonal representations proposed in this paper and Chernozhukov et al. (2016), and extend the initial version of the paper, Hirshberg and Wager (2017), that had only considered the ATE example.”
Approximate residual balancing: debiased inference of average treatment effects in high dimensions (2016, methodology): “Xi/}; see Hirshberg and Wager (2017) and Wang and Zubizarreta (2017) for a discussion and further results.”
Minimax Weight and Q-Function Learning for Off-Policy Evaluation (2019, background): “…functions and distributions in linear programming for MDPs. Formalizing this intuition is an interesting direction.14 13Such a trade-off is relatively well understood in contextual bandits (Kallus, 2020; Hirshberg and Wager, 2017), though extension to sequential decision-making is not obvious.”
Minimal dispersion approximately balancing weights: asymptotic properties and practical considerations (2017, methodology): “While this approach has been shown to work well in practice (Zubizarreta, 2015; Athey et al., 2016; Hirshberg and Wager, 2017), to the best of our knowledge, the formal properties of MABW have not been established.”
Incremental causal effects (2019, methodology): “In Hirshberg and Wager (2017), this is achieved under Donsker assumptions, while Chernozhukov et al. (2018) employ a sparsity assumption on either the approximation of the Riesz representer or the approximation to the regression function.”
Synthetic Difference In Differences (2018, unknown): “…some similarity to the work on “balancing” methods for program evaluation under unconfoundedness, including Athey, Imbens, and Wager ( 2018 ) ; Graham, Pinto, and Egel ( 2012 ) ; Hirshberg and Wager ( 2017 ) ; Imai and Ratkovic ( 2014 ) ; Kallus ( 2020 ) ; Zhao ( 2019 ); and Zubizarreta ( 2015 ) .”