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07/27/2023
RCT Rejection Sampling for Causal Estimation Evaluation
Confounding is a significant obstacle to unbiased estimation of causal e...
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06/08/2023
Causal Inference With Outcome-Dependent Missingness And Self-Censoring
We consider missingness in the context of causal inference when the outc...
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04/04/2023
Graphical Models of Entangled Missingness
Despite the growing interest in causal and statistical inference for set...
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01/27/2023
Ananke: A Python Package For Causal Inference Using Graphical Models
We implement Ananke: an object-oriented Python package for causal infere...
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10/11/2022
Causal and counterfactual views of missing data models
It is often said that the fundamental problem of causal inference is a m...
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03/01/2022
On Testability of the Front-Door Model via Verma Constraints
The front-door criterion can be used to identify and compute causal effe...
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02/28/2022
On Testability and Goodness of Fit Tests in Missing Data Models
Significant progress has been made in developing identification and esti...
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10/14/2020
Differentiable Causal Discovery Under Unmeasured Confounding
The data drawn from biological, economic, and social systems are often c...
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04/10/2020
Full Law Identification In Graphical Models Of Missing Data: Completeness Results
Missing data has the potential to affect analyses conducted in all field...
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03/27/2020
Semiparametric Inference For Causal Effects In Graphical Models With Hidden Variables
The last decade witnessed the development of algorithms that completely ...
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06/29/2019
Identification In Missing Data Models Represented By Directed Acyclic Graphs
Missing data is a pervasive problem in data analyses, resulting in datas...
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06/29/2019