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

August 12, 2022

Probabilistic Variational Causal Approach in Observational Studies
Usef Faghihi, Amir Saki
Causal Effect Causal Relation Causal Analysis Causal Reasoning Novel Causal Model Variational Causal

August 9, 2022

Long-term Causal Effects Estimation via Latent Surrogates Representation Learning
Ruichu Cai, Weilin Chen, Zeqin Yang, Shu Wan, Chen Zheng, Xiaoqing Yang, Jiecheng Guo
Surrogate Model Long Term Causal Effect Causal Structure Surrogate Modeling Surrogate Model Training

July 27, 2022

Causal foundations of bias, disparity and fairness
V. A. Traag, L. Waltman
Procedural Fairness Absolute Stance Bias Gender Bias Causal Effect Racial Bias Significant Disparity

June 23, 2022

Explanatory causal effects for model agnostic explanations
Jiuyong Li, Ha Xuan Tran, Thuc Duy Le, Lin Liu, Kui Yu, Jixue Liu
Causal Graph Causal Effect Model Agnostic Explanation

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 13, 2022

Estimating Causal Effects Under Image Confounding Bias with an Application to Poverty in Africa
Connor T. Jerzak, Fredrik Johansson, Adel Daoud
Application Proficiency Causal Inference Causal Effect Causal Effect Estimation Observational Data Propensity Score Observational Study Poverty Level Biased Causal Effect

June 4, 2022

Discovering Ancestral Instrumental Variables for Causal Inference from Observational Data
Debo Cheng, Jiuyong Li, Lin Liu, Kui Yu, Thuc Duy Lee, Jixue Liu
Causal Inference Causal Effect Observational Data Instrumental Variable

May 29, 2022

Generalization bounds and algorithms for estimating conditional average treatment effect of dosage
Alexis Bellot, Anish Dhir, Giulia Prando
Practical Algorithm Causal Effect Generalization Bound High Quality Counterfactuals Conditional Average Treatment Effect

May 27, 2022

CEBaB: Estimating the Causal Effects of Real-World Concepts on NLP Model Behavior
Eldar David Abraham, Karel D'Oosterlinck, Amir Feder, Yair Ori Gat, Atticus Geiger, Christopher Potts, Roi Reichart, Zhengxuan Wu
Natural Language Processing Real World Causal Effect Natural Language Processing Model Concept Based Opinion Mining Counterfactual Path

May 19, 2022

Deep Learning Methods for Proximal Inference via Maximum Moment Restriction
Benjamin Kompa, David R. Bellamy, Thomas Kolokotrones, James M. Robins, Andrew L. Beam
Deep Learning Causal Effect Unobserved Confounders Unmeasured Confounding Conditional Moment Restriction

May 11, 2022

What is Proxy Discrimination?
Michael Carl Tschantz
Causal Effect Optimization Proxy Transparent Proxy Server Architecture Statistical Dependency

May 8, 2022

Investigating Generalization by Controlling Normalized Margin
Alexander R. Farhang, Jeremy Bernstein, Kushal Tirumala, Yang Liu, Yisong Yue
Strong Generalization Gaussian Process Causal Effect Margin Maximization Standard Deep

May 4, 2022

Experimental Design for Causal Effect Identification
Sina Akbari, Jalal Etesami, Negar Kiyavash
Causal Effect Experimental Design Polynomial Time NP Complete Causal Effect Identification Discrete Logarithm

April 24, 2022

Partial Identification of Dose Responses with Hidden Confounders
Myrl G. Marmarelis, Elizabeth Haddad, Andrew Jesson, Neda Jahanshad, Aram Galstyan, Greg Ver Steeg
Causal Effect Treatment Effect Latent Confounding Dose Response Valued Treatment Marginal Sensitivity Model

April 21, 2022

Scalable Sensitivity and Uncertainty Analysis for Causal-Effect Estimates of Continuous-Valued Interventions
Andrew Jesson, Alyson Douglas, Peter Manshausen, Maëlys Solal, Nicolai Meinshausen, Philip Stier, Yarin Gal, Uri Shalit
Uncertainty Estimation Causal Effect Health Intervention Sensitivity Aware Dose Response Valued Treatment Marginal Sensitivity Model

April 6, 2022

Marrying Fairness and Explainability in Supervised Learning
Przemyslaw Grabowicz, Nicholas Perello, Aarshee Mishra
High Explainability Supervised Learning Causal Effect Shapley Additive Explanation Group DIscrimination Fairness Aware Machine Direct Discrimination

April 2, 2022

Model-Free and Model-Based Policy Evaluation when Causality is Uncertain
David Bruns-Smith
Causal Effect Policy Evaluation Causal Pattern Model Free Unobserved Confounders Uncertain Reasoning

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 3, 2022

Identification in Tree-shaped Linear Structural Causal Models
Benito van der Zander, Marcel Wienöbst, Markus Bläser, Maciej Liśkiewicz
Person Identification Causal Effect Instrumental Variable Structural Equation Structural Characterization Causal Parameter

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