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

November 18, 2024

Causal Effect of Group Diversity on Redundancy and Coverage in Peer-Reviewing
Navita Goyal, Ivan Stelmakh, Nihar Shah, Hal Daumé III
Causal Effect Diversity Awareness Information Redundancy Peer Review Higher Coverage Rate Human Reviewer Co Authorship Network Diverse Evaluation

November 17, 2024

Program Evaluation with Remotely Sensed Outcomes
Ashesh Rambachan, Rahul Singh, Davide Viviano
Remote Sensing Causal Effect Treatment Effect Recent Research Treatment Variable

November 15, 2024

Graph-based Complexity for Causal Effect by Empirical Plug-in
Rina Dechter, Annie Raichev, Alexander Ihler, Jin Tian
Causal Graph Causal Effect Graphical Model Causal Query Generic Plugin

November 10, 2024

CRTRE: Causal Rule Generation with Target Trial Emulation Framework
Junda Wang, Weijian Li, Han Wang, Hanjia Lyu, Caroline P. Thirukumaran, Addisu Mesfin, Hong Yu, Jiebo Luo
Causal Inference Causal Effect Model Interpretability Causal Event Randomized Controlled Trial

November 5, 2024

Controlling for Unobserved Confounding with Large Language Model Classification of Patient Smoking Status
Samuel Lee, Zach Wood-Doughty
Large Language Model Classification Code Causal Inference Causal Effect Unobserved Confounders Causal Interpretation Unmeasured Confounding Unobservable State

November 2, 2024

Marginal Causal Flows for Validation and Inference
Daniel de Vassimon Manela, Laura Battaglia, Robin J. Evans
Scientific Inference Causal Discovery Causal Effect External Validation Causal View Unmeasured Confounding Graphical Normalizing Flow

October 31, 2024

Average Controlled and Average Natural Micro Direct Effects in Summary Causal Graphs
Simon Ferreira, Charles K. Assaad
Causal Inference Causal Graph Causal Effect Causal System Average Performance Summary Causal Graph

October 29, 2024

Bayesian Counterfactual Prediction Models for HIV Care Retention with Incomplete Outcome and Covariate Information
Arman Oganisian, Joseph Hogan, Edwin Sang, Allison DeLong, Ben Mosong, Hamish Fraser, Ann Mwangi
Causal Inference Causal Effect Relevant Covariates Multiple Outcome Counterfactual Prediction Confounding Bias Optimal Scheduling

October 28, 2024

October 21, 2024

Estimating Individual Dose-Response Curves under Unobserved Confounders from Observational Data
Shutong Chen, Yang Li
Causal Effect Latent Variable Observational Data Unobserved Confounders Text Based Confounders Dose Response

October 18, 2024

October 10, 2024

Learning Low-Level Causal Relations using a Simulated Robotic Arm
Miroslav Cibula, Matthias Kerzel, Igor Farkaš
Causal Effect Causal Relation Causal Learning Intelligent Robot Sensorimotor Control

October 9, 2024

Bounds and Sensitivity Analysis of the Causal Effect Under Outcome-Independent MNAR Confounding
Jose M. Peña
Causal Effect Relative Probability Text Based Confounders Sharp Bound Canary Exposure Missingness Shift Missing Not at Random

October 7, 2024

Are causal effect estimations enough for optimal recommendations under multitreatment scenarios?
Sherly Alfonso-Sánchez, Kristina P. Sendova, Cristián Bravo
Causal Effect Treatment Effect Causal Effect Estimation Propensity Score Value at Risk

October 1, 2024

Inferring Kernel $ε$-Machines: Discovering Structure in Complex Systems
Alexandra M. Jurgens, Nicolas Brodu
Causal Effect Causal Structure Complex System Turing Machine Structure Discovery Bayesian Kernel Causal Objective Function

September 24, 2024

Towards Representation Learning for Weighting Problems in Design-Based Causal Inference
Oscar Clivio, Avi Feller, Chris Holmes
Representation Learning Causal Inference Causal Effect Causal Inference Task Dynamic Weight Optimal Weight

September 22, 2024

Causal Inference with Double/Debiased Machine Learning for Evaluating the Health Effects of Multiple Mismeasured Pollutants
Gang Xu, Xin Zhou, Molin Wang, Boya Zhang, Wenhao Jiang, Francine Laden, Helen H. Suh, Adam A. Szpiro, Donna Spiegelman, Zuoheng Wang
Machine Learning Causal Inference Causal Effect Pollution Level

September 16, 2024

A Simple Model to Estimate Sharing Effects in Social Networks
Olivier Jeunen
Causal Effect Social Network Randomized Controlled Trial Randomization Program Simple Model

Causal Effect

Papers

Causal Effect of Group Diversity on Redundancy and Coverage in Peer-Reviewing

Program Evaluation with Remotely Sensed Outcomes

Graph-based Complexity for Causal Effect by Empirical Plug-in

CRTRE: Causal Rule Generation with Target Trial Emulation Framework

Controlling for Unobserved Confounding with Large Language Model Classification of Patient Smoking Status

Marginal Causal Flows for Validation and Inference

Average Controlled and Average Natural Micro Direct Effects in Summary Causal Graphs

Bayesian Counterfactual Prediction Models for HIV Care Retention with Incomplete Outcome and Covariate Information

Deep Learning Methods for the Noniterative Conditional Expectation G-Formula for Causal Inference from Complex Observational Data

Estimating Causal Effects of Text Interventions Leveraging LLMs

Estimating Individual Dose-Response Curves under Unobserved Confounders from Observational Data

Isolated Causal Effects of Natural Language

Estimating the Causal Effects of T Cell Receptors

Learning Low-Level Causal Relations using a Simulated Robotic Arm

Bounds and Sensitivity Analysis of the Causal Effect Under Outcome-Independent MNAR Confounding

Are causal effect estimations enough for optimal recommendations under multitreatment scenarios?

Inferring Kernel $ε$-Machines: Discovering Structure in Complex Systems

Towards Representation Learning for Weighting Problems in Design-Based Causal Inference

Causal Inference with Double/Debiased Machine Learning for Evaluating the Health Effects of Multiple Mismeasured Pollutants

A Simple Model to Estimate Sharing Effects in Social Networks