19 citations · 77 across the 16 of their papers we have counts for
4 papers · 1 filter
RCT Rejection Sampling for Causal Estimation Evaluation
Katherine A. Keith, Sergey Feldman, David Jurgens +2
Confounding is a significant obstacle to unbiased estimation of causal effects from observational data. For settings with high-dimensional covariates -- such as text data, genomics…
Causal Inference With Outcome-Dependent Missingness And Self-Censoring
Jacob M Chen, Daniel Malinsky, Rohit Bhattacharya
We consider missingness in the context of causal inference when the outcome of interest may be missing. If the outcome directly affects its own missingness status, i.e., it is "sel…
Graphical Models of Entangled Missingness
Ranjani Srinivasan, Rohit Bhattacharya, Razieh Nabi +2
Despite the growing interest in causal and statistical inference for settings with data dependence, few methods currently exist to account for missing data in dependent data settin…
Ananke: A Python Package For Causal Inference Using Graphical Models
Jaron J. R. Lee, Rohit Bhattacharya, Razieh Nabi +1
We implement Ananke: an object-oriented Python package for causal inference with graphical models. At the top of our inheritance structure is an easily extensible Graph class that…