b'Ilya Shpitser'

research

∙ 04/10/2023

Partial Identification of Causal Effects Using Proxy Variables

Proximal causal inference is a recently proposed framework for evaluatin...

0 AmirEmad Ghassami, et al. ∙

research

∙ 04/04/2023

Graphical Models of Entangled Missingness

Despite the growing interest in causal and statistical inference for set...

0 Ranjani Srinivasan, et al. ∙

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...

0 Jaron J. R. Lee, et al. ∙

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...

0 Razieh Nabi, et al. ∙

research

∙ 07/27/2022

The Lauritzen-Chen Likelihood For Graphical Models

Graphical models such as Markov random fields (MRFs) that are associated...

0 Ilya Shpitser, et al. ∙

research

∙ 07/18/2022

A self-censoring model for multivariate nonignorable nonmonotone missing data

We introduce a self-censoring model for multivariate nonignorable nonmon...

0 Yilin Li, et al. ∙

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 ...

0 AmirEmad Ghassami, et al. ∙

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...

12 Nathan Drenkow, et al. ∙

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...

0 AmirEmad Ghassami, et al. ∙

research

∙ 10/24/2021

Partially Intervenable Causal Models

Graphical causal models led to the development of complete non-parametri...

0 AmirEmad Ghassami, et al. ∙

research

∙ 09/28/2021

An Automated Approach to Causal Inference in Discrete Settings

When causal quantities cannot be point identified, researchers often pur...

0 Guilherme Duarte, et al. ∙

research

∙ 09/24/2021

Proximal mediation analysis

A common concern when trying to draw causal inferences from observationa...

0 Oliver Dukes, et al. ∙

research

∙ 08/15/2021

The Proximal ID Algorithm

Unobserved confounding is a fundamental obstacle to establishing valid c...

0 Ilya Shpitser, et al. ∙

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...

0 Noam Finkelstein, et al. ∙

research

∙ 05/19/2021

Multiply Robust Causal Mediation Analysis with Continuous Treatments

In many applications, researchers are interested in the direct and indir...

0 AmirEmad Ghassami, et al. ∙

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...

0 Daniel O. Scharfstein, et al. ∙

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...

0 AmirEmad Ghassami, et al. ∙

research

∙ 02/10/2021

Generating Synthetic Text Data to Evaluate Causal Inference Methods

Drawing causal conclusions from observational data requires making assum...

18 Zach Wood-Doughty, et al. ∙

research

∙ 12/23/2020

Partial Identifiability in Discrete Data With Measurement Error

When data contains measurement errors, it is necessary to make assumptio...

0 Noam Finkelstein, et al. ∙

research

∙ 10/14/2020

Differentiable Causal Discovery Under Unmeasured Confounding

The data drawn from biological, economic, and social systems are often c...

0 Rohit Bhattacharya, et al. ∙

research

∙ 08/24/2020

Path Dependent Structural Equation Models

Causal analyses of longitudinal data generally assume structure that is ...

0 Ranjani Srinivasan, et al. ∙

research

∙ 08/13/2020

An Interventionist Approach to Mediation Analysis

Judea Pearl's insight that, when errors are assumed independent, the Pur...

0 James M. Robins, et al. ∙

research

∙ 08/13/2020

Multivariate Counterfactual Systems And Causal Graphical Models

Among Judea Pearl's many contributions to Causality and Statistics, the ...

0 Ilya Shpitser, et al. ∙

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...

0 Noam Finkelstein, et al. ∙

research

∙ 06/08/2020

A Semiparametric Approach to Interpretable Machine Learning

Black box models in machine learning have demonstrated excellent predict...

0 Numair Sani, et al. ∙

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....

0 Numair Sani, et al. ∙

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 ...

0 Mia S. Tackney, et al. ∙

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...

0 Razieh Nabi, et al. ∙

research

∙ 04/02/2020

General Identification of Dynamic Treatment Regimes Under Interference

In many applied fields, researchers are often interested in tailoring tr...

0 Eli Sherman, et al. ∙

research

∙ 04/02/2020

Identification Methods With Arbitrary Interventional Distributions as Inputs

Causal inference quantifies cause-effect relationships by estimating cou...

0 Jaron J. R. Lee, et al. ∙

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 ...

0 Rohit Bhattacharya, et al. ∙

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...

0 Elizabeth L. Ogburn, et al. ∙

research

∙ 10/11/2019

Comment on "Blessings of Multiple Causes"

The premise of the deconfounder method proposed in "Blessings of Multipl...

0 Elizabeth L. Ogburn, et al. ∙

research

∙ 10/09/2019

Optimal Training of Fair Predictive Models

Recently there has been sustained interest in modifying prediction algor...

0 Razieh Nabi, et al. ∙

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...

0 Daniel Malinsky, et al. ∙

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...

6 Rohit Bhattacharya, et al. ∙

research

∙ 06/29/2019

Causal Inference Under Interference And Network Uncertainty

Classical causal and statistical inference methods typically assume the ...

0 Rohit Bhattacharya, et al. ∙

research

∙ 05/20/2019

Conditionally-additive-noise Models for Structure Learning

Constraint-based structure learning algorithms infer the causal structur...

0 Daniel Chicharro, et al. ∙

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...

0 Daniel Malinsky, et al. ∙

research

∙ 02/04/2019

Identification and Estimation of Causal Effects from Dependent Data

The assumption that data samples are independent and identically distrib...

0 Eli Sherman, et al. ∙

research

∙ 12/12/2018

Causal inference, social networks, and chain graphs

Traditionally, statistical and causal inference on human subjects relies...

0 Elizabeth L. Ogburn, et al. ∙

research

∙ 10/01/2018

Challenges of Using Text Classifiers for Causal Inference

Causal understanding is essential for many kinds of decision-making, but...

0 Zach Wood-Doughty, et al. ∙

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...

0 Razieh Nabi, et al. ∙

research

∙ 09/06/2018

Learning Optimal Fair Policies

We consider the problem of learning optimal policies from observational ...

0 Razieh Nabi, et al. ∙

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...

0 Isabel R. Fulcher, et al. ∙

research

∙ 05/29/2017

Fair Inference On Outcomes

In this paper, we consider the problem of fair statistical inference inv...

0 Razieh Nabi, et al. ∙

research

∙ 09/26/2013

Sparse Nested Markov models with Log-linear Parameters

Hidden variables are ubiquitous in practical data analysis, and therefor...

0 Ilya Shpitser, et al. ∙

research

∙ 04/02/2013

On the definition of a confounder

The causal inference literature has provided a clear formal definition o...

0 Tyler J. VanderWeele, et al. ∙

research

∙ 07/20/2012

Parameter and Structure Learning in Nested Markov Models

The constraints arising from DAG models with latent variables can be nat...

0 Ilya Shpitser, et al. ∙

research

∙ 05/09/2012

Effects of Treatment on the Treated: Identification and Generalization

Many applications of causal analysis call for assessing, retrospectively...

0 Ilya Shpitser, et al. ∙

Ilya Shpitser

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