research
∙
07/07/2023
When does the ID algorithm fail?
The ID algorithm solves the problem of identification of interventional ...
share
research
∙
04/10/2023
Partial Identification of Causal Effects Using Proxy Variables
Proximal causal inference is a recently proposed framework for evaluatin...
research
∙
04/04/2023
Graphical Models of Entangled Missingness
Despite the growing interest in causal and statistical inference for set...
research
∙
01/27/2023
Ananke: A Python Package For Causal Inference Using Graphical Models
We implement Ananke: an object-oriented Python package for causal infere...
research
∙
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...
research
∙
07/27/2022
The Lauritzen-Chen Likelihood For Graphical Models
Graphical models such as Markov random fields (MRFs) that are associated...
research
∙
07/18/2022
A self-censoring model for multivariate nonignorable nonmonotone missing data
We introduce a self-censoring model for multivariate nonignorable nonmon...
research
∙
01/26/2022
Combining Experimental and Observational Data for Identification and Estimation of Long-Term Causal Effects
We consider the task of identifying and estimating the causal effect of ...
research
∙
12/01/2021
Robustness in Deep Learning for Computer Vision: Mind the gap?
Deep neural networks for computer vision tasks are deployed in increasin...
research
∙
11/04/2021
Proximal Causal Inference with Hidden Mediators: Front-Door and Related Mediation Problems
Proximal causal inference was recently proposed as a framework to identi...
research
∙
10/24/2021
Partially Intervenable Causal Models
Graphical causal models led to the development of complete non-parametri...
research
∙
09/28/2021
An Automated Approach to Causal Inference in Discrete Settings
When causal quantities cannot be point identified, researchers often pur...
research
∙
09/24/2021
Proximal mediation analysis
A common concern when trying to draw causal inferences from observationa...
research
∙
08/15/2021
The Proximal ID Algorithm
Unobserved confounding is a fundamental obstacle to establishing valid c...
research
∙
07/15/2021
Entropic Inequality Constraints from e-separation Relations in Directed Acyclic Graphs with Hidden Variables
Directed acyclic graphs (DAGs) with hidden variables are often used to c...
research
∙
05/19/2021
Multiply Robust Causal Mediation Analysis with Continuous Treatments
In many applications, researchers are interested in the direct and indir...
research
∙
05/19/2021
Markov-Restricted Analysis of Randomized Trials with Non-Monotone Missing Binary Outcomes: Sensitivity Analysis and Identification Results
Scharfstein et al. (2021) developed a sensitivity analysis model for ana...
research
∙
04/07/2021
Minimax Kernel Machine Learning for a Class of Doubly Robust Functionals
A moment function is called doubly robust if it is comprised of two nuis...
research
∙
02/10/2021
Generating Synthetic Text Data to Evaluate Causal Inference Methods
Drawing causal conclusions from observational data requires making assum...
research
∙
12/23/2020
Partial Identifiability in Discrete Data With Measurement Error
When data contains measurement errors, it is necessary to make assumptio...
research
∙
10/14/2020
Differentiable Causal Discovery Under Unmeasured Confounding
The data drawn from biological, economic, and social systems are often c...
research
∙
08/24/2020
Path Dependent Structural Equation Models
Causal analyses of longitudinal data generally assume structure that is ...
research
∙
08/13/2020
An Interventionist Approach to Mediation Analysis
Judea Pearl's insight that, when errors are assumed independent, the Pur...
research
∙
08/13/2020
Multivariate Counterfactual Systems And Causal Graphical Models
Among Judea Pearl's many contributions to Causality and Statistics, the ...
research
∙
07/01/2020
Deriving Bounds and Inequality Constraints Using LogicalRelations Among Counterfactuals
Causal parameters may not be point identified in the presence of unobser...
research
∙
06/08/2020
A Semiparametric Approach to Interpretable Machine Learning
Black box models in machine learning have demonstrated excellent predict...
research
∙
06/03/2020
Explaining The Behavior Of Black-Box Prediction Algorithms With Causal Learning
We propose to explain the behavior of black-box prediction methods (e.g....
research
∙
05/27/2020
Nonmyopic and pseudo-nonmyopic approaches to optimal sequential design in the presence of covariates
In sequential experiments, subjects become available for the study over ...
research
∙
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...
research
∙
04/02/2020
General Identification of Dynamic Treatment Regimes Under Interference
In many applied fields, researchers are often interested in tailoring tr...
research
∙
04/02/2020
Identification Methods With Arbitrary Interventional Distributions as Inputs
Causal inference quantifies cause-effect relationships by estimating cou...
research
∙
03/27/2020
Semiparametric Inference For Causal Effects In Graphical Models With Hidden Variables
The last decade witnessed the development of algorithms that completely ...
research
∙
01/17/2020
Counterexamples to "The Blessings of Multiple Causes" by Wang and Blei
This brief note is meant to complement our previous comment on "The Bles...
research
∙
10/11/2019
Comment on "Blessings of Multiple Causes"
The premise of the deconfounder method proposed in "Blessings of Multipl...
research
∙
10/09/2019
Optimal Training of Fair Predictive Models
Recently there has been sustained interest in modifying prediction algor...
research
∙
09/04/2019
Semiparametric Inference for Non-monotone Missing-Not-at-Random Data: the No Self-Censoring Model
We study the identification and estimation of statistical functionals of...
research
∙
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...
research
∙
06/29/2019
Causal Inference Under Interference And Network Uncertainty
Classical causal and statistical inference methods typically assume the ...
research
∙
05/20/2019
Conditionally-additive-noise Models for Structure Learning
Constraint-based structure learning algorithms infer the causal structur...
research
∙
03/08/2019
A Potential Outcomes Calculus for Identifying Conditional Path-Specific Effects
The do-calculus is a well-known deductive system for deriving connection...
research
∙
02/04/2019
Identification and Estimation of Causal Effects from Dependent Data
The assumption that data samples are independent and identically distrib...
research
∙
12/12/2018
Causal inference, social networks, and chain graphs
Traditionally, statistical and causal inference on human subjects relies...
research
∙
10/01/2018
Challenges of Using Text Classifiers for Causal Inference
Causal understanding is essential for many kinds of decision-making, but...
research
∙
09/27/2018
Estimation of Personalized Effects Associated With Causal Pathways
The goal of personalized decision making is to map a unit's characterist...
research
∙
09/06/2018
Learning Optimal Fair Policies
We consider the problem of learning optimal policies from observational ...
research
∙
11/09/2017
The generalized front-door criterion for estimation of indirect causal effects of a confounded treatment
The population intervention effect (PIE) of an exposure measures the exp...
research
∙
05/29/2017
Fair Inference On Outcomes
In this paper, we consider the problem of fair statistical inference inv...
research
∙
09/26/2013
Sparse Nested Markov models with Log-linear Parameters
Hidden variables are ubiquitous in practical data analysis, and therefor...
research
∙
04/02/2013
On the definition of a confounder
The causal inference literature has provided a clear formal definition o...
research
∙
07/20/2012
