Average Causal Effect

Average causal effect (ACE) estimation aims to quantify the causal impact of an intervention on an outcome, accounting for confounding factors. Current research focuses on improving estimation accuracy and robustness, particularly when dealing with unobserved confounders, continuous treatment variables, and complex data structures, employing methods like deep learning models, graphical normalizing flows, and targeted minimum loss-based estimation. These advancements are crucial for reliable causal inference across diverse fields, enabling more accurate evaluations of interventions in areas such as medicine, public policy, and machine learning model explainability.

Papers

October 8, 2024

Ordering-Based Causal Discovery for Linear and Nonlinear Relations
Zhuopeng Xu, Yujie Li, Cheng Liu, Ning Gui
Causal Discovery Causal Relation Linear Ordered Data Causal Discovery Algorithm Average Causal Effect Topological Ordering

September 6, 2024

Average Causal Effect Estimation in DAGs with Hidden Variables: Extensions of Back-Door and Front-Door Criteria
Anna Guo, Razieh Nabi
Backdoor Attack Causal Effect Directed Acyclic Graph Semi Parametric Functional Theory Nuisance Parameter Front Door Criterion Average Causal Effect

April 3, 2024

Effector: A Python package for regional explanations
Vasilis Gkolemis, Christos Diou, Eirini Ntoutsi, Theodore Dalamagas, Bernd Bischl, Julia Herbinger, Giuseppe Casalicchio
Python Library Average Causal Effect Feature Effect

November 19, 2023

Causal ATE Mitigates Unintended Bias in Controlled Text Generation
Rahul Madhavan, Kahini Wadhawan
Language Model Text Generation Causal Event Average Causal Effect

September 20, 2023

RHALE: Robust and Heterogeneity-aware Accumulated Local Effects
Vasilis Gkolemis, Theodore Dalamagas, Eirini Ntoutsi, Christos Diou
Estimation Bias Heterogeneous Effect Average Causal Effect Local Effect

November 29, 2022

Causal Inference with Conditional Instruments using Deep Generative Models
Debo Cheng, Ziqi Xu, Jiuyong Li, Lin Liu, Jixue Liu, Thuc Duy Le
Causal Inference Instrumental Variable Latent Confounders Conditional Expectation Average Causal Effect Treatment Variable

October 14, 2022

Partial Identification of Treatment Effects with Implicit Generative Models
Vahid Balazadeh, Vasilis Syrgkanis, Rahul G. Krishnan
Causal Graph Treatment Effect Structural Causal Model Implicit Generative Average Causal Effect

September 15, 2022

$\rho$-GNF : A Novel Sensitivity Analysis Approach Under Unobserved Confounders
Sourabh Balgi, Jose M. Peña, Adel Daoud
Causal Inference Causal Effect Unobserved Confounders Sensitivity Analysis Average Causal Effect Gaussian Copula Graphical Normalizing Flow

March 8, 2022

Estimating the average causal effect of intervention in continuous variables using machine learning
Yoshiaki Kitazawa
Machine Learning Causal Effect Novel Identifiability Test Continuous Variable Average Treatment Effect Early Intervention Average Causal Effect

March 2, 2022

Estimating average causal effects from patient trajectories
Dennis Frauen, Tobias Hatt, Valentyn Melnychuk, Stefan Feuerriegel
Causal Effect Time Varying Patient Trajectory Average Causal Effect

February 7, 2022

Personalized Public Policy Analysis in Social Sciences using Causal-Graphical Normalizing Flows
Sourabh Balgi, Jose M. Pena, Adel Daoud
Causal Effect Social Science Counterfactual Inference Average Causal Effect Online Policy Customization Graphical Normalizing Flow

December 26, 2021

Long Story Short: Omitted Variable Bias in Causal Machine Learning
Victor Chernozhukov, Carlos Cinelli, Whitney Newey, Amit Sharma, Vasilis Syrgkanis
Covariate Shift Causal Machine Learning Causal Machine Systematic Bias Average Causal Effect Long Story Causal Parameter