Zobrazeno 1 - 10
of 62
pro vyhledávání: '"Bhattacharya, Rohit"'
Autor:
Wu, Yufeng, Bhattacharya, Rohit
A key question in many network studies is whether the observed correlations between units are primarily due to contagion or latent confounding. Here, we study this question using a segregated graph (Shpitser, 2015) representation of these mechanisms,
Externí odkaz:
http://arxiv.org/abs/2411.01371
Recent text-based causal methods attempt to mitigate confounding bias by estimating proxies of confounding variables that are partially or imperfectly measured from unstructured text data. These approaches, however, assume analysts have supervised la
Externí odkaz:
http://arxiv.org/abs/2401.06687
Prior work applying semiparametric theory to causal inference has primarily focused on deriving estimators that exhibit statistical robustness under a prespecified causal model that permits identification of a desired causal parameter. However, a fun
Externí odkaz:
http://arxiv.org/abs/2310.10393
Publikováno v:
Transactions on Machine Learning Research (TMLR) 2023
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, or the behavioral social sciences -- researchers have proposed metho
Externí odkaz:
http://arxiv.org/abs/2307.15176
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 "self-censoring", this may lead to severely biased causal effect estimates
Externí odkaz:
http://arxiv.org/abs/2306.05511
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 settings; most classical missing data methods in statistics and causal infer
Externí odkaz:
http://arxiv.org/abs/2304.01953
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 provides an interface to several broadly useful graph-based algorithms
Externí odkaz:
http://arxiv.org/abs/2301.11477
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 difficult because for every experimental unit only one potential response is
Externí odkaz:
http://arxiv.org/abs/2210.05558
Autor:
Bhattacharya, Rohit, Nabi, Razieh
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 assumptions -- (i) the existence of a variable (or set of variables) that fu
Externí odkaz:
http://arxiv.org/abs/2203.00161
Autor:
Nabi, Razieh, Bhattacharya, Rohit
Publikováno v:
Proceedings of the 39th Conference on Uncertainty in Artificial Intelligence (UAI), 2023
Significant progress has been made in developing identification and estimation techniques for missing data problems where modeling assumptions can be described via a directed acyclic graph. The validity of results using such techniques rely on the as
Externí odkaz:
http://arxiv.org/abs/2203.00132