Causal Inference

Causal inference aims to determine cause-and-effect relationships from data, going beyond mere correlations to understand how interventions impact outcomes. Current research heavily focuses on addressing challenges like confounding (the influence of unobserved variables), particularly in high-dimensional data and complex treatments (e.g., text, sequences of actions), employing methods such as structural causal models, Bayesian Additive Regression Trees (BART), and various neural network architectures including Graph Neural Networks (GNNs). These advancements are crucial for improving the reliability of causal conclusions across diverse fields, from medicine and economics to personalized interventions and policy-making.

Papers

May 25, 2023

Dynamic Inter-treatment Information Sharing for Individualized Treatment Effects Estimation
Vinod Kumar Chauhan, Jiandong Zhou, Ghadeer Ghosheh, Soheila Molaei, David A. Clifton
Causal Inference Treatment Effect Limited Data Treatment Assignment Treatment Effect Estimation

May 24, 2023

Operationalizing Counterfactual Metrics: Incentives, Ranking, and Information Asymmetry
Serena Wang, Stephen Bates, P. M. Aronow, Michael I. Jordan
Causal Inference Partial Ranking Information Asymmetry Institutional Incentive Counterfactual Metric

May 23, 2023

Covariate balancing using the integral probability metric for causal inference
Insung Kong, Yuha Park, Joonhyuk Jung, Kwonsang Lee, Yongdai Kim
Causal Inference Relevant Covariates Propensity Score ApeRIodic SEmi Parametric Optimal Weight

May 18, 2023

Estimation Beyond Data Reweighting: Kernel Method of Moments
Heiner Kremer, Yassine Nemmour, Bernhard Schölkopf, Jia-Jie Zhu
Causal Inference Estimation Task Kernel Method Temporal Moment Data Reweighting Conditional Moment Restriction

May 17, 2023

May 11, 2023

Integrating Nearest Neighbors with Neural Network Models for Treatment Effect Estimation
Niki Kiriakidou, Christos Diou
Deep Learning Causal Inference Causal Effect Neural Network Model Treatment Effect

May 9, 2023

COLA: Contextualized Commonsense Causal Reasoning from the Causal Inference Perspective
Zhaowei Wang, Quyet V. Do, Hongming Zhang, Jiayao Zhang, Weiqi Wang, Tianqing Fang, Yangqiu Song, Ginny Y. Wong, Simon See
Causal Inference Commonsense Causality

May 5, 2023

Causal Discovery with Stage Variables for Health Time Series
Bharat Srikishan, Samantha Kleinberg
Causal Inference Causal Discovery Causal Relationship Causal Relation

May 4, 2023

String Diagrams with Factorized Densities
Eli Sennesh, Jan-Willem van de Meent
Causal Inference Causal Model Probabilistic Programming Probability MAP Probabilistic Program Unnormalized Density String Diagram

April 24, 2023

Causal Effect Estimation with Variational AutoEncoder and the Front Door Criterion
Ziqi Xu, Debo Cheng, Jiuyong Li, Jixue Liu, Lin Liu, Kui Yu
Causal Inference Variational Autoencoder Causal Effect Estimation Unobserved Confounders Causal Strength Front Door Criterion

April 17, 2023

April 5, 2023

A step towards the applicability of algorithms based on invariant causal learning on observational data
Borja Guerrero Santillan
Practical Algorithm Causal Inference Causal Discovery Observational Data Causal Discovery Method Sample STEP Applicability Study Optimal Prediction Invariant Causal Feature Invariant Causal

March 24, 2023

March 20, 2023

Approaching an unknown communication system by latent space exploration and causal inference
Gašper Beguš, Andrej Leban, Shane Gero
Latent Space Causal Inference Meaningful Representation Causal Disentanglement Unsupervised Generative Animal Communication

March 7, 2023

Learning When to Treat Business Processes: Prescriptive Process Monitoring with Causal Inference and Reinforcement Learning
Zahra Dasht Bozorgi, Marlon Dumas, Marcello La Rosa, Artem Polyvyanyy, Mahmoud Shoush, Irene Teinemaa
Reinforcement Learning LeArning Abstract Causal Inference Decision Making Business Process Learning Mechanism Continuous Improvement Prescriptive Process Monitoring

March 6, 2023

Environment Invariant Linear Least Squares
Jianqing Fan, Cong Fang, Yihong Gu, Tong Zhang
Causal Inference Least Square Global Endogenous Variate Conditional Invariance

March 3, 2023

PAGE: A Position-Aware Graph-Based Model for Emotion Cause Entailment in Conversation
Xiaojie Gu, Renze Lou, Lin Sun, Shangxin Li
Causal Inference Target Emotion Causal Relation Potential Conversation Outcome Web Page Causal Emotion Entailment Position Aware Graph

Causal Inference

Papers

Dynamic Inter-treatment Information Sharing for Individualized Treatment Effects Estimation

Operationalizing Counterfactual Metrics: Incentives, Ranking, and Information Asymmetry

Covariate balancing using the integral probability metric for causal inference

Estimation Beyond Data Reweighting: Kernel Method of Moments

The Impact of Missing Data on Causal Discovery: A Multicentric Clinical Study

A Survey on Causal Discovery: Theory and Practice

Integrating Nearest Neighbors with Neural Network Models for Treatment Effect Estimation

COLA: Contextualized Commonsense Causal Reasoning from the Causal Inference Perspective

Causal Discovery with Stage Variables for Health Time Series

String Diagrams with Factorized Densities

Causal Effect Estimation with Variational AutoEncoder and the Front Door Criterion

pgmpy: A Python Toolkit for Bayesian Networks

Compositional Probabilistic and Causal Inference using Tractable Circuit Models

A step towards the applicability of algorithms based on invariant causal learning on observational data

Synthetic Combinations: A Causal Inference Framework for Combinatorial Interventions

Differentially Private Synthetic Control

Approaching an unknown communication system by latent space exploration and causal inference

Learning When to Treat Business Processes: Prescriptive Process Monitoring with Causal Inference and Reinforcement Learning

Environment Invariant Linear Least Squares

PAGE: A Position-Aware Graph-Based Model for Emotion Cause Entailment in Conversation