Papers

Estimating Heterogeneous Causal Mediation Effects with Bayesian Decision Tree Ensembles

Angela Ting, Antonio R. Linero

Design-Based Causal Inference with Missing Outcomes: Missingness Mechanisms, Imputation-Assisted Randomization Tests, and Covariate Adjustment

Siyu Heng, Jiawei Zhang, Yang Feng

Incorporating Auxiliary Variables to Improve the Efficiency of Time-Varying Treatment Effect Estimation

Jieru Shi, Zhenke Wu, Walter Dempsey

Causal Inference for Genomic Data with Multiple Heterogeneous Outcomes

Jin-Hong Du, Zhenghao Zeng, Edward H. Kennedy et al.

Manipulating an Instrumental Variable in an Observational Study of Premature Babies: Design, Bounds, and Inference

Zhe Chen, Min Haeng Cho, Bo Zhang

Federated Adaptive Causal Estimation (FACE) of Target Treatment Effects

Larry Han, Jue Hou, Kelly Cho et al.

Discovering the Network Granger Causality in Large Vector Autoregressive Models

Yoshimasa Uematsu, Takashi Yamagata

On the Comparative Analysis of Average Treatment Effects Estimation via Data Combination

Peng Wu, Shanshan Luo, Zhi Geng

Matrix Completion When Missing Is Not at Random and Its Applications in Causal Panel Data Models

Jungjun Choi, Ming Yuan

Causal Effect Estimation Under Network Interference with Mean-Field Methods

Sohom Bhattacharya, Subhabrata Sen

Causality Pursuit from Heterogeneous Environments via Neural Adversarial Invariance Learning

Yihong Gu, Cong Fang, Peter Bühlmann et al.

A Common-Cause Principle for Eliminating Selection Bias in Causal Estimands Through Covariate Adjustment

Maya Mathur, Ilya Shpitser, Tyler VanderWeele

Score-based Causal Representation Learning: Linear and General Transformations

Burak Var{{\i}}c{{\i}}, Emre Acartürk, Karthikeyan Shanmugam et al.

This paper addresses intervention-based causal representation learning (CRL) under a general nonparametric latent causal model and an unknown transfor...

Causal Effect of Functional Treatment

Ruoxu Tan, Wei Huang, Zheng Zhang et al.

We study the causal effect with a functional treatment variable, where practical applications often arise in neuroscience, biomedical sciences, etc. P...

Causal Abstraction: A Theoretical Foundation for Mechanistic Interpretability

Atticus Geiger, Duligur Ibeling, Amir Zur et al.

Causal abstraction provides a theoretical foundation for mechanistic interpretability, the field concerned with providing intelligible algorithms that...

Learning causal graphs via nonlinear sufficient dimension reduction

Eftychia Solea, Bing Li, Kyongwon Kim

We introduce a new nonparametric methodology for estimating a directed acyclic graph (DAG) from observational data. Our method is nonparametric in nat...

Recursive Causal Discovery

Ehsan Mokhtarian, Sepehr Elahi, Sina Akbari et al.

Causal discovery from observational data, i.e., learning the causal graph from a finite set of samples from the joint distribution of the variables, i...

DAGs as Minimal I-maps for the Induced Models of Causal Bayesian Networks under Conditioning

Xiangdong Xie, Jiahua Guo, Yi Sun

Bayesian networks (BNs) are a powerful tool for knowledge representation and reasoning, especially for complex systems. A critical task in the applic...

Efficient and Robust Semi-supervised Estimation of Average Treatment Effect with Partially Annotated Treatment and Response

Jue Hou, Rajarshi Mukherjee, Tianxi Cai

A notable challenge of leveraging Electronic Health Records (EHR) for treatment effect assessment is the lack of precise information on important clin...

Copula-based Sensitivity Analysis for Multi-Treatment Causal Inference with Unobserved Confounding

Jiajing Zheng, Alexander D'Amour, Alexander Franks

Recent work has focused on the potential and pitfalls of causal identification in observational studies with multiple simultaneous treatments. Buildin...

Optimal Experiment Design for Causal Effect Identification

Sina Akbari, Jalal Etesami, Negar Kiyavash

Pearl’s do calculus is a complete axiomatic approach to learn the identifiable causal effects from observational data. When such an effect is not iden...

Directed Cyclic Graphs for Simultaneous Discovery of Time-Lagged and Instantaneous Causality from Longitudinal Data Using Instrumental Variables

Wei Jin, Yang Ni, Amanda B. Spence et al.

We consider the problem of causal discovery from longitudinal observational data. We develop a novel framework that simultaneously discovers the time-...

Locally Private Causal Inference for Randomized Experiments

Yuki Ohnishi, Jordan Awan

Local differential privacy is a differential privacy paradigm in which individuals first apply a privacy mechanism to their data (often by adding nois...

Estimating Network-Mediated Causal Effects via Principal Components Network Regression

Alex Hayes, Mark M. Fredrickson, Keith Levin

We develop a method to decompose causal effects on a social network into an indirect effect mediated by the network, and a direct effect independent o...

DisC2o-HD: Distributed causal inference with covariates shift for analyzing real-world high-dimensional data

Jiayi Tong, Jie Hu, George Hripcsak et al.

High-dimensional healthcare data, such as electronic health records (EHR) data and claims data, present two primary challenges due to the large number...

Correction to: Parameterizing and simulating from causal models

Identification and multiply robust estimation in causal mediation analysis across principal strata

Chao Cheng, Fan Li

Structural restrictions in local causal discovery: identifying direct causes of a target variable

J Bodik, V Chavez-Demoulin

Abstract

Covariate-assisted bounds on causal effects with instrumental variables

Alexander W Levis, others

Improving efficiency in transporting average treatment effects

K E Rudolph, others

Abstract

Adaptive experiments toward learning treatment effect heterogeneity

Waverly Wei, others