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

August 11, 2022

Valid Inference after Causal Discovery
Paula Gradu, Tijana Zrnic, Yixin Wang, Michael I. Jordan
Scientific Inference Causal Inference Causal Discovery Causal Effect Estimation

August 8, 2022

Recovering the Graph Underlying Networked Dynamical Systems under Partial Observability: A Deep Learning Approach
Sérgio Machado, Anirudh Sridhar, Paulo Gil, Jorge Henriques, José M. F. Moura, Augusto Santos
Causal Inference Dynamical System Deep Learning Approach Partial Observability Graph Structure Learning Synthetic Network

July 29, 2022

Tangential Wasserstein Projections
Florian Gunsilius, Meng Hsuan Hsieh, Myung Jin Lee
Causal Inference Generalized Geodesic Wasserstein Space Probability Measure Multivariate Probability Distribution Projection Robust Wasserstein

July 19, 2022

DESCN: Deep Entire Space Cross Networks for Individual Treatment Effect Estimation
Kailiang Zhong, Fengtong Xiao, Yan Ren, Yaorong Liang, Wenqing Yao, Xiaofeng Yang, Ling Cen
Causal Inference Treatment Effect Individual Treatment Effect Treatment Effect Estimation Treatment Selection Bias

July 11, 2022

Functional Generalized Empirical Likelihood Estimation for Conditional Moment Restrictions
Heiner Kremer, Jia-Jie Zhu, Krikamol Muandet, Bernhard Schölkopf
Causal Inference Conditional Moment Restriction

July 10, 2022

On the Representation of Causal Background Knowledge and its Applications in Causal Inference
Zhuangyan Fang, Ruiqi Zhao, Yue Liu, Yangbo He
Financial Application Causal Inference Individual Representation Directed Acyclic Graph Causal Knowledge Causal DAG Explicitly Mentioned Causal Fact

July 6, 2022

On The Universality of Diagrams for Causal Inference and The Causal Reproducing Property
Sridhar Mahadevan
Causal Inference K$ Universality Fundamental Diagram Actual Causality

July 4, 2022

June 20, 2022

Measuring the Effect of Training Data on Deep Learning Predictions via Randomized Experiments
Jinkun Lin, Anqi Zhang, Mathias Lecuyer, Jinyang Li, Aurojit Panda, Siddhartha Sen
Deep Learning Model Training Data Mixed Effect Causal Inference Shapley Value Randomized Experiment LASSO Regression

June 18, 2022

Causal Inference with Treatment Measurement Error: A Nonparametric Instrumental Variable Approach
Yuchen Zhu, Limor Gultchin, Arthur Gretton, Matt Kusner, Ricardo Silva
Causal Inference Causal Effect Novel Regression Kernel Method Measurement Error Kernel Based Estimator Characteristic Function Nonparametric Instrumental Variable

June 17, 2022

Reframed GES with a Neural Conditional Dependence Measure
Xinwei Shen, Shengyu Zhu, Jiji Zhang, Shoubo Hu, Zhitang Chen
Causal Inference Causal Discovery Causal Structure Underlying Causal Convolutional Conditional Neural Process Greedy Equivalence Search

June 16, 2022

June 14, 2022

DoWhy-GCM: An extension of DoWhy for causal inference in graphical causal models
Patrick Blöbaum, Peter Götz, Kailash Budhathoki, Atalanti A. Mastakouri, Dominik Janzing
Causal Inference Causal Graph Extension Study Causal Query Causal Graphical Model Visual Causality Discovery

June 13, 2022

June 9, 2022

A Simple Unified Approach to Testing High-Dimensional Conditional Independences for Categorical and Ordinal Data
Ankur Ankan, Johannes Textor
Causal Inference Unified Framework Conditional Independence Graphical Model Ordinal Pattern Category Shift Independence Testing

June 5, 2022

Estimating and Mitigating the Congestion Effect of Curbside Pick-ups and Drop-offs: A Causal Inference Approach
Xiaohui Liu, Sean Qian, Hock-Hai Teo, Wei Ma
Causal Inference Estimation Task Traffic Flow Congestion Mitigation Urban Road Network

June 4, 2022

Combinatorial Causal Bandits
Shi Feng, Wei Chen
Causal Inference Causal Structure Generalized Linear Model Causal Bandit

Causal Inference

Papers

Valid Inference after Causal Discovery

Recovering the Graph Underlying Networked Dynamical Systems under Partial Observability: A Deep Learning Approach

Tangential Wasserstein Projections

DESCN: Deep Entire Space Cross Networks for Individual Treatment Effect Estimation

Functional Generalized Empirical Likelihood Estimation for Conditional Moment Restrictions

On the Representation of Causal Background Knowledge and its Applications in Causal Inference

On The Universality of Diagrams for Causal Inference and The Causal Reproducing Property

A Causal Approach for Business Optimization: Application on an Online Marketplace

Breaking Feedback Loops in Recommender Systems with Causal Inference

Measuring the Effect of Training Data on Deep Learning Predictions via Randomized Experiments

Causal Inference with Treatment Measurement Error: A Nonparametric Instrumental Variable Approach

Reframed GES with a Neural Conditional Dependence Measure

Towards Understanding How Machines Can Learn Causal Overhypotheses

Continuous-Time Modeling of Counterfactual Outcomes Using Neural Controlled Differential Equations

DoWhy-GCM: An extension of DoWhy for causal inference in graphical causal models

Estimating Causal Effects Under Image Confounding Bias with an Application to Poverty in Africa

Causal Inference-Based Root Cause Analysis for Online Service Systems with Intervention Recognition

A Simple Unified Approach to Testing High-Dimensional Conditional Independences for Categorical and Ordinal Data

Estimating and Mitigating the Congestion Effect of Curbside Pick-ups and Drop-offs: A Causal Inference Approach

Combinatorial Causal Bandits