Boosting Causal Additive Models

Maximilian Kertel, Nadja Klein

We present a boosting-based method to learn additive Structural Equation Models (SEMs) from observational data, with a focus on the theoretical aspect...

Maximum Causal Entropy IRL in Mean-Field Games and GNEP Framework for Forward RL

Berkay Anahtarci, Can Deha Kariksiz, Naci Saldi

This paper explores the use of Maximum Causal Entropy Inverse Reinforcement Learning (IRL) within the context of discrete-time stationary Mean-Field G...

Degree of Interference: A General Framework For Causal Inference Under Interference

Yuki Ohnishi, Bikram Karmakar, Arman Sabbaghi

One core assumption typically adopted for valid causal inference is that of no interference between experimental units, i.e., the outcome of an experi...

Causality-oriented robustness: exploiting general noise interventions in linear structural causal models

Peter Bühlmann, Xinwei Shen, Armeen Taeb

Confidence Sets for Causal Orderings

Y. Samuel Wang, Mladen Kolar, Mathias Drton

The Effect of Alcohol intake on Brain White Matter Microstructural Integrity: A New Causal Inference Framework for Incomplete Phenomic Data

Shuo Chen, Chixiang Chen, Zhenyao Ye et al.

Berry-Esseen Bounds for Design-Based Causal Inference With Possibly Diverging Treatment Levels and Varying Group Sizes

Peng Ding, Lei Shi

Causal Effect Estimation Under Network Interference with Mean-Field Methods

Subhabrata Sen, Sohom Bhattacharya

Causality Pursuit from Heterogeneous Environments via Neural Adversarial Invariance Learning

Jianqing Fan, Yihong Gu, Cong Fang et al.

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

Ilya Shpitser, Maya Mathur, 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, Tianxi Cai, Rajarshi Mukherjee

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

Negar Kiyavash, Sina Akbari, Jalal Etesami

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

Jordan Awan, Yuki Ohnishi

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...

Harmonized estimation of subgroup-specific treatment effects in randomized trials: the use of external control data

Daniel Schwartz, others

Design and analysis of randomized trials to estimate spatio-temporally heterogeneous treatment effects

Samuel I. Watson, Thomas A. Smith

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

Bo Zhang, Zhe Chen, Min Haeng Cho

Debiased learning of the causal net benefit with censored event time data

Torben Martinussen, Stijn Vansteelandt

Abstract

Correction to: Parameterizing and simulating from causal models

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

Chao Cheng, Fan Li

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, Yang Feng, Jiawei Zhang

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

Larry Wasserman, Jin-Hong Du, Zhenghao Zeng et al.

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

Federated Adaptive Causal Estimation (FACE) of Target Treatment Effects

Jue Hou, Tianxi Cai, Rui Duan et al.

Discovering the Network Granger Causality in Large Vector Autoregressive Models

Yoshimasa Uematsu, Takashi Yamagata

Adaptive experiments toward learning treatment effect heterogeneity

Waverly Wei, others

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

Ming Yuan, Jungjun Choi