19 citations · 77 across the 16 of their papers we have counts for
16 papers
Robust Weighted Triangulation of Causal Effects Under Model Uncertainty
Rohit Bhattacharya, Ina Ocelli, Ted Westling
A fundamental challenge in causal inference with observational data is correct specification of a causal model. When there is model uncertainty, analysts may seek to use estimates…
Response to Discussions of "Causal and Counterfactual Views of Missing Data Models"
Razieh Nabi, Rohit Bhattacharya, Ilya Shpitser +1
We are grateful to the discussants, Levis and Kennedy [2025], Luo and Geng [2025], Wang and van der Laan [2025], and Yang and Kim [2025], for their thoughtful comments on our paper…
Recursive Equations For Imputation Of Missing Not At Random Data With Sparse Pattern Support
Trung Phung, Kyle Reese, Ilya Shpitser +1
A common approach for handling missing values in data analysis pipelines is multiple imputation via software packages such as MICE (Van Buuren and Groothuis-Oudshoorn, 2011) and Am…
Proximal Causal Inference With Text Data
Jacob M. Chen, Rohit Bhattacharya, Katherine A. Keith
Recent text-based causal methods attempt to mitigate confounding bias by estimating proxies of confounding variables that are partially or imperfectly measured from unstructured te…
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…
Causal and Counterfactual Views of Missing Data Models
Razieh Nabi, Rohit Bhattacharya, Ilya Shpitser +1
It is often said that the fundamental problem of causal inference is a missing data problem -- the comparison of responses to two hypothetical treatment assignments is made difficu…
On Testability of the Front-Door Model via Verma Constraints
Rohit Bhattacharya, Razieh Nabi
The front-door criterion can be used to identify and compute causal effects despite the existence of unmeasured confounders between a treatment and outcome. However, the key assump…
On Testability and Goodness of Fit Tests in Missing Data Models
Razieh Nabi, Rohit Bhattacharya
Significant progress has been made in developing identification and estimation techniques for missing data problems where modeling assumptions can be described via a directed acycl…
Differentiable Causal Discovery Under Unmeasured Confounding
Rohit Bhattacharya, Tushar Nagarajan, Daniel Malinsky +1
The data drawn from biological, economic, and social systems are often confounded due to the presence of unmeasured variables. Prior work in causal discovery has focused on discret…
Full Law Identification In Graphical Models Of Missing Data: Completeness Results
Razieh Nabi, Rohit Bhattacharya, Ilya Shpitser
Missing data has the potential to affect analyses conducted in all fields of scientific study, including healthcare, economics, and the social sciences. Several approaches to unbia…
Semiparametric Inference For Causal Effects In Graphical Models With Hidden Variables
Rohit Bhattacharya, Razieh Nabi, Ilya Shpitser
Identification theory for causal effects in causal models associated with hidden variable directed acyclic graphs (DAGs) is well studied. However, the corresponding algorithms are…
Identification In Missing Data Models Represented By Directed Acyclic Graphs
Rohit Bhattacharya, Razieh Nabi, Ilya Shpitser +1
Missing data is a pervasive problem in data analyses, resulting in datasets that contain censored realizations of a target distribution. Many approaches to inference on the target…
Causal Inference Under Interference And Network Uncertainty
Rohit Bhattacharya, Daniel Malinsky, Ilya Shpitser
Classical causal and statistical inference methods typically assume the observed data consists of independent realizations. However, in many applications this assumption is inappro…