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

October 18, 2024

A Novel Method to Metigate Demographic and Expert Bias in ICD Coding with Causal Inference
Bin Zhang, Junli Wang
Causal Inference New Method Demographic Attribute ICD Code International Classification of Disease Expert Observation

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

October 13, 2024

DAG-aware Transformer for Causal Effect Estimation
Manqing Liu, David R. Bellamy, Andrew L. Beam
Causal Inference Causal Structure Causal Effect Estimation Directed Acyclic Graph Cause Effect Structure Aware Transformer

October 11, 2024

October 7, 2024

October 1, 2024

Causal Representation Learning with Generative Artificial Intelligence: Application to Texts as Treatments
Kosuke Imai, Kentaro Nakamura
Application Proficiency Causal Inference Generative Artificial Intelligence Text Based Causal Representation Learning Causal Representation Accurate Treatment

September 27, 2024

Using Deep Autoregressive Models as Causal Inference Engines
Daniel Jiwoong Im, Kevin Zhang, Nakul Verma, Kyunghyun Cho
Scientific Inference Causal Inference Time Varying Unobserved Confounders

September 26, 2024

Detecting and Measuring Confounding Using Causal Mechanism Shifts
Abbavaram Gowtham Reddy, Vineeth N Balasubramanian
Data Detection Causal Inference Causal Discovery Causal Direction Confounding Bias Causal Attribution

September 24, 2024

September 23, 2024

From Text to Treatment Effects: A Meta-Learning Approach to Handling Text-Based Confounding
Henri Arno, Paloma Rabaey, Thomas Demeester
Text Modality Constructive Approach Causal Inference Treatment Effect Text Based Conditional Average Treatment Effect Causal Machine Learning Meta Learner

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 15, 2024

Causal Inference with Large Language Model: A Survey
Jing Ma
Large Language Model Timely Survey Causal Inference Causal Relation Causal Inference Task

September 13, 2024

Causal GNNs: A GNN-Driven Instrumental Variable Approach for Causal Inference in Networks
Xiaojing Du, Feiyu Yang, Wentao Gao, Xiongren Chen
Graph Neural Network Causal Inference Network Programming Causal Effect Estimation Instrumental Variable Latent Confounding

September 10, 2024

Advancing Causal Inference: A Nonparametric Approach to ATE and CATE Estimation with Continuous Treatments
Hugo Gobato Souto, Francisco Louzada Neto
Causal Inference Treatment Effect Conditional Average Treatment Effect Generative Transformer Model BART Valued Treatment Nonparametric Approach

September 9, 2024

Causal Inference

Papers

A Novel Method to Metigate Demographic and Expert Bias in ICD Coding with Causal Inference

Do LLMs Have the Generalization Ability in Conducting Causal Inference?

A Practical Approach to Causal Inference over Time

DAG-aware Transformer for Causal Effect Estimation

Learning Representations of Instruments for Partial Identification of Treatment Effects

DiffPO: A causal diffusion model for learning distributions of potential outcomes

Neural Networks Decoded: Targeted and Robust Analysis of Neural Network Decisions via Causal Explanations and Reasoning

Mitigating Modality Prior-Induced Hallucinations in Multimodal Large Language Models via Deciphering Attention Causality

Causal Representation Learning with Generative Artificial Intelligence: Application to Texts as Treatments

Using Deep Autoregressive Models as Causal Inference Engines

Detecting and Measuring Confounding Using Causal Mechanism Shifts

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

Linear Contextual Bandits with Interference

From Text to Treatment Effects: A Meta-Learning Approach to Handling Text-Based Confounding

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

Causal Inference with Large Language Model: A Survey

Causal GNNs: A GNN-Driven Instrumental Variable Approach for Causal Inference in Networks

Advancing Causal Inference: A Nonparametric Approach to ATE and CATE Estimation with Continuous Treatments

K-Fold Causal BART for CATE Estimation

Beyond Flatland: A Geometric Take on Matching Methods for Treatment Effect Estimation