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Found 5 papers, 4 papers with code
Stable Estimation of Survival Causal Effects
Our experiments on synthetic and semi-synthetic data demonstrate that our method has competitive bias and smaller variance than debiased machine learning approaches.
Off-Policy Evaluation via Adaptive Weighting with Data from Contextual Bandits
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.
Confidence Intervals for Policy Evaluation in Adaptive Experiments
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.
Synthetic Difference in Differences
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
Augmented Minimax Linear Estimation
Many statistical estimands can expressed as continuous linear functionals of a conditional expectation function.
Methodology 62F12
