Tailored inference for finite populations: conditional validity and transfer across distributions
View details for DOI 10.1093/biomet/asad022
View details for Web of Science ID 000984341300001
Sensitivity analysis of individual treatment effects: A robust conformal inference approach.
Proceedings of the National Academy of Sciences of the United States of America
2023; 120 (6): e2214889120
We propose a model-free framework for sensitivity analysis of individual treatment effects (ITEs), building upon ideas from conformal inference. For any unit, our procedure reports the Gamma-value, a number which quantifies the minimum strength of confounding needed to explain away the evidence for ITE. Our approach rests on the reliable predictive inference of counterfactuals and ITEs in situations where the training data are confounded. Under the marginal sensitivity model of [Z. Tan, J. Am. Stat. Assoc. 101, 1619-1637 (2006)], we characterize the shift between the distribution of the observations and that of the counterfactuals. We first develop a general method for predictive inference of test samples from a shifted distribution; we then leverage this to construct covariate-dependent prediction sets for counterfactuals. No matter the value of the shift, these prediction sets (resp. approximately) achieve marginal coverage if the propensity score is known exactly (resp. estimated). We describe a distinct procedure also attaining coverage, however, conditional on the training data. In the latter case, we prove a sharpness result showing that for certain classes of prediction problems, the prediction intervals cannot possibly be tightened. We verify the validity and performance of the methods via simulation studies and apply them to analyze real datasets.
View details for DOI 10.1073/pnas.2214889120
View details for PubMedID 36730196
Toward Optimal Variance Reduction in Online Controlled Experiments
View details for DOI 10.1080/00401706.2022.2142670
View details for Web of Science ID 000910693900001