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

November 3, 2023

High Precision Causal Model Evaluation with Conditional Randomization
Chao Ma, Cheng Zhang
Causal Inference Conditional Randomization

October 31, 2023

October 30, 2023

Generator Identification for Linear SDEs with Additive and Multiplicative Noise
Yuanyuan Wang, Xi Geng, Wei Huang, Biwei Huang, Mingming Gong
Causal Inference Synchronous Generator Multiplicative Noise Parameter Identifiability Feed Additive Interventional Distribution

October 26, 2023

Causal Modeling with Stationary Diffusions
Lars Lorch, Andreas Krause, Bernhard Schölkopf
Diffusion Model Causal Inference Causal Graph Causal Model Stochastic Differential Equation

October 25, 2023

October 24, 2023

Causal Understanding of Why Users Share Hate Speech on Social Media
Dominique Geissler, Abdurahman Maarouf, Stefan Feuerriegel
Social Medium Causal Inference Hate Speech User Base Social Medium Data Propensity Score Causal Interpretation

October 23, 2023

Causal Inference Using LLM-Guided Discovery
Aniket Vashishtha, Abbavaram Gowtham Reddy, Abhinav Kumar, Saketh Bachu, Vineeth N Balasubramanian, Amit Sharma
Causal Inference Causal Graph Causal Discovery Algorithm Causal Order

October 21, 2023

Counterfactual Prediction Under Selective Confounding
Sohaib Kiani, Jared Barton, Jon Sushinsky, Lynda Heimbach, Bo Luo
Causal Inference Counterfactual Prediction Text Based Confounders Interpretable Causal Non Confounding Covariates

October 17, 2023

Assessing the Causal Impact of Humanitarian Aid on Food Security
Jordi Cerdà-Bautista, José María Tárraga, Vasileios Sitokonstantinou, Gustau Camps-Valls
Causal Inference Causal Effect Causal Model Humanitarian Response Food Security

October 10, 2023

Differentially Private Multi-Site Treatment Effect Estimation
Tatsuki Koga, Kamalika Chaudhuri, David Page
Differential Privacy Causal Inference Medical AI Private Estimation

October 9, 2023

High Dimensional Causal Inference with Variational Backdoor Adjustment
Daniel Israel, Aditya Grover, Guy Van den Broeck
High Dimensional Causal Inference Variational Inference Backdoor Effect High Dimensional Causal

October 5, 2023

Causal Inference in Gene Regulatory Networks with GFlowNet: Towards Scalability in Large Systems
Trang Nguyen, Alexander Tong, Kanika Madan, Yoshua Bengio, Dianbo Liu
Large Scale Causal Inference Causal Discovery Potential Scalability Causal Structure Regulatory Network SWIFT DynGFN

October 3, 2023

October 1, 2023

Towards Causal Foundation Model: on Duality between Causal Inference and Attention
Jiaqi Zhang, Joel Jennings, Agrin Hilmkil, Nick Pawlowski, Cheng Zhang, Chao Ma
Foundation Model Human Attention Causal Inference Causal Learning Strong Duality Zero Shot Causal

September 30, 2023

CausalImages: An R Package for Causal Inference with Earth Observation, Bio-medical, and Social Science Images
Connor T. Jerzak, Adel Daoud
Causal Inference Social Science R Package

September 22, 2023

OpportunityFinder: A Framework for Automated Causal Inference
Huy Nguyen, Prince Grover, Devashish Khatwani
New Framework Causal Inference Emerging Opportunity Causal Effect Causal Analysis Causal Effect Identification

Causal Inference

Papers

High Precision Causal Model Evaluation with Conditional Randomization

Text-Transport: Toward Learning Causal Effects of Natural Language

An Introduction to Causal Inference Methods for Observational Human-Robot Interaction Research

Generator Identification for Linear SDEs with Additive and Multiplicative Noise

Causal Modeling with Stationary Diffusions

Causal Q-Aggregation for CATE Model Selection

CATE Lasso: Conditional Average Treatment Effect Estimation with High-Dimensional Linear Regression

Local Discovery by Partitioning: Polynomial-Time Causal Discovery Around Exposure-Outcome Pairs

Causal Understanding of Why Users Share Hate Speech on Social Media

Causal Inference Using LLM-Guided Discovery

Counterfactual Prediction Under Selective Confounding

Assessing the Causal Impact of Humanitarian Aid on Food Security

Differentially Private Multi-Site Treatment Effect Estimation

High Dimensional Causal Inference with Variational Backdoor Adjustment

Causal Inference in Gene Regulatory Networks with GFlowNet: Towards Scalability in Large Systems

Causal Inference with Conditional Front-Door Adjustment and Identifiable Variational Autoencoder

Conditional Instrumental Variable Regression with Representation Learning for Causal Inference

Towards Causal Foundation Model: on Duality between Causal Inference and Attention

CausalImages: An R Package for Causal Inference with Earth Observation, Bio-medical, and Social Science Images

OpportunityFinder: A Framework for Automated Causal Inference