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Found 11 papers, 3 papers with code
Proximal Causal Inference With Text Data
Recent text-based causal methods attempt to mitigate confounding bias by including unstructured text data as proxies of confounding variables that are partially or imperfectly measured.
RCT Rejection Sampling for Causal Estimation Evaluation
We contribute a new sampling algorithm, which we call RCT rejection sampling, and provide theoretical guarantees that causal identification holds in the observational data to allow for valid comparisons to the ground-truth RCT.
Causal and counterfactual views of missing data models
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 observed.
On Testability of the Front-Door Model via Verma Constraints
The front-door criterion can be used to identify and compute causal effects despite the existence of unmeasured confounders between a treatment and outcome.
On Testability and Goodness of Fit Tests in Missing Data Models
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.
Differentiable Causal Discovery Under Unmeasured Confounding
In this work, we derive differentiable algebraic constraints that fully characterize the space of ancestral ADMGs, as well as more general classes of ADMGs, arid ADMGs and bow-free ADMGs, that capture all equality restrictions on the observed variables.
Path Dependent Structural Equation Models
Causal analyses of longitudinal data generally assume that the qualitative causal structure relating variables remains invariant over time.
Full Law Identification In Graphical Models Of Missing Data: Completeness Results
Missing data has the potential to affect analyses conducted in all fields of scientific study, including healthcare, economics, and the social sciences.
Semiparametric Inference For Causal Effects In Graphical Models With Hidden Variables
We derive influence function based estimators that exhibit double robustness for the identified effects in a large class of hidden variable DAGs where the treatment satisfies a simple graphical criterion; this class includes models yielding the adjustment and front-door functionals as special cases.
Causal Inference Under Interference And Network Uncertainty
Classical causal and statistical inference methods typically assume the observed data consists of independent realizations.
Identification In Missing Data Models Represented By Directed Acyclic Graphs
Missing data is a pervasive problem in data analyses, resulting in datasets that contain censored realizations of a target distribution.
