179 citations · 182 across the 3 of their papers we have counts for
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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,…
Learning Gaussian Graphical Models with Latent Confounders
Ke Wang, Alexander Franks, Sang-Yun Oh
Gaussian Graphical models (GGM) are widely used to estimate the network structures in many applications ranging from biology to finance. In practice, data is often corrupted by lat…
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…