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 12, 2024

Language Models as Causal Effect Generators
Lucius E.J. Bynum, Kyunghyun Cho
Language Model Causal Inference Causal Structure Causal Model Structural Causal Model Novel Causal Model

November 11, 2024

Estimating Causal Effects in Partially Directed Parametric Causal Factor Graphs
Malte Luttermann, Tanya Braun, Ralf Möller, Marcel Gehrke
Graph Drawing Causal Inference Causal Relationship Causal Effect Estimation Causal Direction Statistical Relational Parametric Causal

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

November 3, 2024

Educational Effects in Mathematics: Conditional Average Treatment Effect depending on the Number of Treatments
Tomoko Nagai, Takayuki Okuda, Tomoya Nakamura, Yuichiro Sato, Yusuke Sato, Kensaku Kinjo, Kengo Kawamura, Shin Kikuta, Naoto Kumano-go
Causal Inference Numerical Data Level Mathematics Conditional Average Treatment Effect Accurate Treatment B Learner Student Learning

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 30, 2024

An Overview of Causal Inference using Kernel Embeddings
Dino Sejdinovic
Causal Inference Causal Relationship Causal Effect Estimation Kernel Hilbert Space Kernel Mean Embeddings

October 29, 2024

October 28, 2024

Deep Learning Methods for the Noniterative Conditional Expectation G-Formula for Causal Inference from Complex Observational Data
Sophia M Rein, Jing Li, Miguel Hernan, Andrew Beam
Deep Learning Causal Inference Causal Effect Generalized Linear Model Mathematical Formula Classical Data Conditional Expectation Deep Neural Network Estimator

October 26, 2024

Mechanism learning: Reverse causal inference in the presence of multiple unknown confounding through front-door causal bootstrapping
Jianqiao Mao, Max A. Little
Machine Learning Causal Inference Speech Presence Causal Machine Learning Causal Variable Learning Mechanism

October 24, 2024

October 22, 2024

LLMScan: Causal Scan for LLM Misbehavior Detection
Mengdi Zhang, Kai Kiat Goh, Peixin Zhang, Jun Sun
Causal Inference Causality Analysis

October 21, 2024

Accounting for Missing Covariates in Heterogeneous Treatment Estimation
Khurram Yamin, Vibhhu Sharma, Ed Kennedy, Bryan Wilder
Scientific Inference Causal Inference Relevant Covariates Heterogeneous Treatment Effect Missing Covariates

October 18, 2024

October 15, 2024

Do LLMs Have the Generalization Ability in Conducting Causal Inference?
Chen Wang, Dongming Zhao, Bo Wang, Ruifang He, Yuexian Hou
Medical LLM Causal Inference Causal Inference Task

October 14, 2024

A Practical Approach to Causal Inference over Time
Martina Cinquini, Isacco Beretta, Salvatore Ruggieri, Isabel Valera
Causal Inference Time Matter Autoregressive Model Causal Model Causal Intervention Practical Approach