Comparing causal parameters with many treatments and positivity violations

A Mcclean, Y Li, S Bae et al.

Summary Comparing outcomes across treatments is essential in medicine and public policy. To do so, researchers typically estimate a ...

Dynamic covariate balancing: estimating treatment effects over time with potential local projections

Jelena Bradic, Davide Viviano

Abstract This article concerns the estimation and inference of treatment effects in panel data settings when treatments change dynam...

Theory for Identification and Inference with Synthetic Controls: A Proximal Causal Inference Framework

Myeonghun Yu, Xu Shi, Arun Kumar Kuchibhotla et al.

The causal effects of modified treatment policies under network interference

Salvador V Balkus, Scott W Delaney, Nima S Hejazi

Abstract Modified treatment policies are a widely applicable class of interventions useful for studying the causal effects of contin...

A causal fused lasso for interpretable heterogeneous treatment effects estimation

Oscar Hernan Madrid Padilla, Yanzhen Chen, Carlos Misael Madrid Padilla et al.

We propose a novel method for estimating heterogeneous treatment effects based on the fused lasso. By first ordering samples based on the propensity o...

Hierarchical Causal Models

Eli N. Weinstein, David M. Blei

Causal questions often arise in settings where data are hierarchical: subunits are nested within units. Consider students in schools, cells in patient...

Flexible Functional Treatment Effect Estimation

Jiayi Wang, Raymond K. W. Wong, Xiaoke Zhang et al.

We study treatment effect estimation with functional treatments where the average potential outcome functional is a function of functions, in contrast...

Identifying Weight-Variant Latent Causal Models

Mingming Gong, Yuhang Liu, Zhen Zhang et al.

The task of causal representation learning aims to uncover latent higher-level causal variables that affect lower-level observations. Identifying the ...

Design-based Causal Inference for Incomplete Block Designs

Taehyeon Koo, Nicole E Pashley

Abstract Researchers often turn to block randomization to increase the precision of their inference or due to practical consideratio...

A Conditional Ordinal Stereotype Model to Estimate Police Officers’ Propensity to Escalate Force

Greg Ridgeway

The impact of job stability on monetary poverty in Italy: causal small area estimation

Dehan Kong, Nicola Salvati, Katarzyna Reluga et al.

Randomization-Based Confidence Sets for the Local Average Treatment Effect

P M Aronow, Haoge Chang, Patrick Lopatto

Summary We consider the problem of generating confidence sets in randomized experiments with noncompliance. We show that a refinemen...

Bounds on causal effects in 2𝑲 factorial experiments with non-compliance

M Blackwell, N E Pashley

Summary Factorial experiments are ubiquitous in the social and biomedical sciences, but when units fail to comply with each assigned...

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

J Bodik, V Chavez-Demoulin

Abstract

Post-selection inference for causal effects after causal discoveryGet access

T Changand others

Towards Interpretable Deep Generative Models via Causal Representation Learning

Gemma Moran, Bryon Aragam

On the Identifying Power of Generalized Monotonicity for Average Treatment Effects*

Yuehao Bai, Shunzhuang Huang, Sarah Moon et al.

Enhanced inference for distributions and quantiles of individual treatment effects in various experiments

Zhe Chen, Xinran Li

Balancing Weights for Causal Inference in Observational Factorial Studies

Peng Ding, Ruoqi Yu

Causal Inference in Pharmaceutical Statistics.

Ashley L. Buchanan

Identification and multiply robust estimation of causal effects via instrumental variables from an auxiliary population

Peng Ding, Zhi Geng, Wei Li et al.

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

Peter Bühlmann, Armeen Taeb, Xinwei Shen

Identifiability of Causal Graphs under Non-Additive Conditionally Parametric Causal Models

Juraj Bodik, Valérie Chavez-Demoulin

Existing approaches to causal discovery often rely on restrictive modeling assumptions that limit their applicability in real-world settings, particul...

On the Representation of Pairwise Causal Background Knowledge and Its Applications in Causal Inference

Zhuangyan Fang, Ruiqi Zhao, Yue Liu et al.

Pairwise causal background knowledge about the existence or absence of causal edges and paths is frequently encountered in observational studies. Such...

Parameter identification in linear non-Gaussian causal models under general confounding

Jalal Etesami, Mathias Drton, Daniele Tramontano

Representation of context-specific causal models with observational and interventional data

Eliana DuarteandLiam Solus

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...

Confidence Sets for Causal Orderings

Mladen Kolar, Y. Samuel Wang, 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

Sina Akbari, Negar Kiyavash, 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

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

Bo Zhang, Zhe Chen, Min Haeng Cho

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

Samuel I. Watson, Thomas A. Smith

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

Yang Feng, Siyu Heng, 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.

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.

An omitted variable bias framework for sensitivity analysis of instrumental variables

Carlos Cinelli, Chad Hazlett

Abstract We develop an omitted variable bias framework for sensitivity analysis of instrumental variable estimates that naturally handl...

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

Jungjun Choi, Ming Yuan