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 27, 2023

A Neural Framework for Generalized Causal Sensitivity Analysis
Dennis Frauen, Fergus Imrie, Alicia Curth, Valentyn Melnychuk, Stefan Feuerriegel, Mihaela van der Schaar
Causal Inference Neural CDEs Neural Framework Marginal Sensitivity Model

November 20, 2023

Designing monitoring strategies for deployed machine learning algorithms: navigating performativity through a causal lens
Jean Feng, Adarsh Subbaswamy, Alexej Gossmann, Harvineet Singh, Berkman Sahiner, Mi-Ok Kim, Gene Pennello, Nicholas Petrick, Romain Pirracchio, Fan Xia
Machine Learning Causal Inference Machine Learning Algorithm Performative Power Monitoring Strategy

November 15, 2023

Causal prediction models for medication safety monitoring: The diagnosis of vancomycin-induced acute kidney injury
Izak Yasrebi-de Kom, Joanna Klopotowska, Dave Dongelmans, Nicolette De Keizer, Kitty Jager, Ameen Abu-Hanna, Giovanni Cinà
Causal Inference Medical Diagnosis Causal Model Causal Direction Causal Prediction Causality Analysis Drug Safety Acute Kidney Injury

November 13, 2023

November 12, 2023

Omitted Labels in Causality: A Study of Paradoxes
Bijan Mazaheri, Siddharth Jain, Matthew Cook, Jehoshua Bruck
Study Feature Causal Inference Causal Pattern Different Context Social Choice Missing Label Fermi Paradox

November 9, 2023

Causal Inference from Text: Unveiling Interactions between Variables
Yuxiang Zhou, Yulan He
Text Modality Causal Inference Causal Effect Latent Feature Relevant Covariates Variable Model Biased Causal Effect Non Confounding Covariates Deep Interaction

November 8, 2023

Causal Inference on Investment Constraints and Non-stationarity in Dynamic Portfolio Optimization through Reinforcement Learning
Yasuhiro Nakayama, Tomochika Sawaki
Reinforcement Learning Causal Inference Non Stationarity Cost Constraint

November 7, 2023

Learned Causal Method Prediction
Shantanu Gupta, Cheng Zhang, Agrin Hilmkil
Causal Inference Causal Discovery Causal Prediction Synthetic Causal

November 3, 2023

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

Causal Inference

Papers

A Neural Framework for Generalized Causal Sensitivity Analysis

Designing monitoring strategies for deployed machine learning algorithms: navigating performativity through a causal lens

Causal prediction models for medication safety monitoring: The diagnosis of vancomycin-induced acute kidney injury

Do large language models and humans have similar behaviors in causal inference with script knowledge?

The Impact of Generative Artificial Intelligence on Market Equilibrium: Evidence from a Natural Experiment

Omitted Labels in Causality: A Study of Paradoxes

Causal Inference from Text: Unveiling Interactions between Variables

Causal Inference on Investment Constraints and Non-stationarity in Dynamic Portfolio Optimization through Reinforcement Learning

Learned Causal Method Prediction

Structured Neural Networks for Density Estimation and Causal Inference

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