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 5, 2024

Controlling for Unobserved Confounding with Large Language Model Classification of Patient Smoking Status
Samuel Lee, Zach Wood-Doughty
Large Language Model Classification Code Causal Inference Causal Effect Unobserved Confounders Causal Interpretation Unmeasured Confounding Unobservable State

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 Student Learning B Learner

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 Conditional Expectation Classical Data 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

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

Neural Networks Decoded: Targeted and Robust Analysis of Neural Network Decisions via Causal Explanations and Reasoning
Alec F. Diallo, Vaishak Belle, Paul Patras
Deep Learning Complex Reasoning Causal Inference Causal Graph Interpretability Method Causal Explanation Causal System Neural Network Decoder

Causal Inference

Papers

Controlling for Unobserved Confounding with Large Language Model Classification of Patient Smoking Status

Educational Effects in Mathematics: Conditional Average Treatment Effect depending on the Number of Treatments

Average Controlled and Average Natural Micro Direct Effects in Summary Causal Graphs

An Overview of Causal Inference using Kernel Embeddings

Bayesian Counterfactual Prediction Models for HIV Care Retention with Incomplete Outcome and Covariate Information

Debiasing Alternative Data for Credit Underwriting Using Causal Inference

Deep Learning Methods for the Noniterative Conditional Expectation G-Formula for Causal Inference from Complex Observational Data

Mechanism learning: Reverse causal inference in the presence of multiple unknown confounding through front-door causal bootstrapping

Cascading Failure Prediction via Causal Inference

Causal Order Discovery based on Monotonic SCMs

LLMScan: Causal Scan for LLM Misbehavior Detection

Accounting for Missing Covariates in Heterogeneous Treatment Estimation

Differentially Private Covariate Balancing Causal Inference

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