Causal Effect

Causal effect estimation aims to determine the impact of an intervention or treatment on an outcome, accounting for confounding factors that might obscure the true relationship. Current research focuses on improving estimation accuracy and robustness, particularly in complex settings with high-dimensional data, multiple treatments, and unobserved variables, employing techniques like double machine learning, graph neural networks, and Bayesian methods. These advancements are crucial for reliable causal inference across diverse fields, enabling more informed decision-making in areas such as healthcare, social sciences, and business, where understanding cause-and-effect relationships is paramount.

Papers

March 17, 2023

Delayed and Indirect Impacts of Link Recommendations
Han Zhang, Shangen Lu, Yixin Wang, Mihaela Curmei
Causal Effect Social Network Significant Delay Change Growth Monitoring Network Evolution Link Recommendation

March 8, 2023

Bayesian Causal Forests for Multivariate Outcomes: Application to Irish Data From an International Large Scale Education Assessment
Nathan McJames, Andrew Parnell, Yong Chen Goh, Ann O'Shea
Application Proficiency Large Scale Causal Effect Available Datasets Causal Machine Learning Causal Forest Bayesian Additive Regression Tree

March 7, 2023

Causal Dependence Plots
Joshua R. Loftus, Lucius E. J. Bynum, Sakina Hansen
Causal Effect Causal Learning Causal Assumption Auxiliary Model

March 2, 2023

Hyperparameter Tuning and Model Evaluation in Causal Effect Estimation
Damian Machlanski, Spyridon Samothrakis, Paul Clarke
Causal Inference Causal Discovery Causal Effect Hyperparameter Tuning Causal Effect Estimation Model Evaluation

February 23, 2023

Understanding the Impact of Competing Events on Heterogeneous Treatment Effect Estimation from Time-to-Event Data
Alicia Curth, Mihaela van der Schaar
Global Impact Causal Effect Treatment Effect Event Description Covariate Shift Heterogeneous Treatment Effect Heterogeneous Treatment Effect Estimation Time to Event Data

February 21, 2023

Estimating long-term causal effects from short-term experiments and long-term observational data with unobserved confounding
Graham Van Goffrier, Lucas Maystre, Ciarán Gilligan-Lee
Estimation Task Long Term Causal Effect

February 19, 2023

Disentangled Representation for Causal Mediation Analysis
Ziqi Xu, Debo Cheng, Jiuyong Li, Jixue Liu, Lin Liu, Ke Wang
Variational Autoencoder Causal Effect Disentangled Representation Latent Confounders Causal Mediation Analysis

February 11, 2023

Sequential Underspecified Instrument Selection for Cause-Effect Estimation
Elisabeth Ailer, Jason Hartford, Niki Kilbertus
High Dimensional Causal Effect Instrumental Variable Cause Effect Treatment Variable

February 10, 2023

Causal Inference out of Control: Estimating the Steerability of Consumption
Gary Cheng, Moritz Hardt, Celestine Mendler-Dünner
External Control Causal Inference Causal Effect Consumption Data Steerable Robot Causal Identifiability

February 6, 2023

Causal Estimation of Exposure Shifts with Neural Networks
Mauricio Tec, Kevin Josey, Oladimeji Mudele, Francesca Dominici
Neural Network Causal Inference Causal Effect Causal Effect Estimation Air Pollution Frequency Response Exposure Data Robust Causal

February 1, 2023

How to select predictive models for causal inference?
Matthieu Doutreligne, Gaël Varoquaux
Causal Inference Feature Selection Predictive Model Causal Effect Causal Inference Task

January 31, 2023

A Reinforcement Learning Framework for Dynamic Mediation Analysis
Lin Ge, Jitao Wang, Chengchun Shi, Zhenke Wu, Rui Song
Causal Effect Causal Mediation Analysis

January 30, 2023

Falsification of Internal and External Validity in Observational Studies via Conditional Moment Restrictions
Zeshan Hussain, Ming-Chieh Shih, Michael Oberst, Ilker Demirel, David Sontag
Causal Effect Latent Confounding Observational Study Conditional Moment Restriction Construct Validity

January 27, 2023

Epsilon-Identifiability of Causal Quantities
Ang Li, Scott Mueller, Judea Pearl
Causal Effect Causal Direction Actual Cause Partial Identifiability Counterfactual Distribution Epsilon Identifiability

January 26, 2023

Estimating Causal Effects using a Multi-task Deep Ensemble
Ziyang Jiang, Zhuoran Hou, Yiling Liu, Yiman Ren, Keyu Li, David Carlson
Gaussian Process Causal Effect Causal Effect Estimation Multimodal Regression

January 18, 2023

Non-parametric identifiability and sensitivity analysis of synthetic control models
Jakob Zeitler, Athanasios Vlontzos, Ciaran M. Gilligan-Lee
Causal Effect Structural Causal Model Sensitivity Analysis Synthetic Control Invariant Causal Synthetic Causal

January 16, 2023

January 1, 2023

An Adaptive Kernel Approach to Federated Learning of Heterogeneous Causal Effects
Thanh Vinh Vo, Arnab Bhattacharyya, Young Lee, Tze-Yun Leong
Causal Effect Decentralized Data Heterogeneous Treatment Effect Adaptive Transfer Learning

December 21, 2022

Debiased machine learning for estimating the causal effect of urban traffic on pedestrian crossing behaviour
Kimia Kamal, Bilal Farooq
New Machine Causal Effect BEHAVIOR Explanation Dimensional Copula Urban Traffic Travel Mode Pedestrian Engagement Pedestrian Crossing