430 citations · 856 across the 28 of their papers we have counts for
7 papers · 1 filter
Sensitivity to Unobserved Confounding in Studies with Factor-structured Outcomes
Jiajing Zheng, Jiaxi Wu, Alexander D'Amour +1
In this work, we propose an approach for assessing sensitivity to unobserved confounding in studies with multiple outcomes. We demonstrate how prior knowledge unique to the multi-o…
Understanding the Risks and Rewards of Combining Unbiased and Possibly Biased Estimators, with Applications to Causal Inference
Michael Oberst, Alexander D'Amour, Minmin Chen +3
Several problems in statistics involve the combination of high-variance unbiased estimators with low-variance estimators that are only unbiased under strong assumptions. A notable…
Bayesian Inference and Partial Identification in Multi-Treatment Causal Inference with Unobserved Confounding
Jiajing Zheng, Alexander D'Amour, Alexander Franks
In causal estimation problems, the parameter of interest is often only partially identified, implying that the parameter cannot be recovered exactly, even with infinite data. Here,…
Deconfounding Scores: Feature Representations for Causal Effect Estimation with Weak Overlap
Alexander D'Amour, Alexander Franks
A key condition for obtaining reliable estimates of the causal effect of a treatment is overlap (a.k.a. positivity): the distributions of the features used to perform causal adjust…
Copula-based Sensitivity Analysis for Multi-Treatment Causal Inference with Unobserved Confounding
Jiajing Zheng, Alexander D'Amour, Alexander Franks
Recent work has focused on the potential and pitfalls of causal identification in observational studies with multiple simultaneous treatments. Building on previous work, we show th…
Comment: Reflections on the Deconfounder
Alexander D'Amour
The aim of this comment (set to appear in a formal discussion in JASA) is to draw out some conclusions from an extended back-and-forth I have had with Wang and Blei regarding the d…
Flexible sensitivity analysis for observational studies without observable implications
Alexander Franks, Alexander D'Amour, Avi Feller
A fundamental challenge in observational causal inference is that assumptions about unconfoundedness are not testable from data. Assessing sensitivity to such assumptions is theref…