no code implementations • 1 Oct 2023 • Khiem Pham, David A. Hirshberg, Phuong-Mai Huynh-Pham, Michele Santacatterina, Ser-Nam Lim, Ramin Zabih
Our experiments on synthetic and semi-synthetic data demonstrate that our method has competitive bias and smaller variance than debiased machine learning approaches.
1 code implementation • 3 Jun 2021 • Ruohan Zhan, Vitor Hadad, David A. Hirshberg, Susan Athey
In particular, when the pattern of treatment assignment in the collected data looks little like the pattern generated by the policy to be evaluated, the importance weights used in DR estimators explode, leading to excessive variance.
1 code implementation • 7 Nov 2019 • Vitor Hadad, David A. Hirshberg, Ruohan Zhan, Stefan Wager, Susan Athey
In this context, typical estimators that use inverse propensity weighting to eliminate sampling bias can be problematic: their distributions become skewed and heavy-tailed as the propensity scores decay to zero.
4 code implementations • 24 Dec 2018 • Dmitry Arkhangelsky, Susan Athey, David A. Hirshberg, Guido W. Imbens, Stefan Wager
We present a new estimator for causal effects with panel data that builds on insights behind the widely used difference in differences and synthetic control methods.
Methodology
2 code implementations • 30 Nov 2017 • David A. Hirshberg, Stefan Wager
Many statistical estimands can expressed as continuous linear functionals of a conditional expectation function.
Methodology 62F12