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

December 12, 2023

Instrumental Variable Estimation for Causal Inference in Longitudinal Data with Time-Dependent Latent Confounders
Debo Cheng, Ziqi Xu, Jiuyong Li, Lin Liu, Jixue Liu, Wentao Gao, Thuc Duy Le
Causal Inference Time Varying Instrumental Variable Longitudinal Data Factor Model

December 10, 2023

Learning the Causal Structure of Networked Dynamical Systems under Latent Nodes and Structured Noise
Augusto Santos, Diogo Rente, Rui Seabra, José M. F. Moura
LeArning Abstract Causal Inference Dynamical System Latent Representation Causal Structure Partial Observability Correlated Noise Causal Network

December 8, 2023

Disentangled Latent Representation Learning for Tackling the Confounding M-Bias Problem in Causal Inference
Debo Cheng, Yang Xie, Ziqi Xu, Jiuyong Li, Lin Liu, Jixue Liu, Yinghao Zhang, Zaiwen Feng
Causal Inference Latent Confounders Bias Related Issue Disentangled Latent

December 7, 2023

December 3, 2023

Marginal Density Ratio for Off-Policy Evaluation in Contextual Bandits
Muhammad Faaiz Taufiq, Arnaud Doucet, Rob Cornish, Jean-Francois Ton
Causal Inference Contextual Bandit Policy Evaluation Inverse Propensity Inverse Probability Weighting

December 1, 2023

SpaCE: The Spatial Confounding Environment
Mauricio Tec, Ana Trisovic, Michelle Audirac, Sophie Woodward, Jie Kate Hu, Naeem Khoshnevis, Francesca Dominici
Causal Inference Causal Discovery Deep Space Spatial Dependence

November 30, 2023

Geometry-Aware Normalizing Wasserstein Flows for Optimal Causal Inference
Kaiwen Hou
Causal Inference Normalizing Flow Wasserstein Gradient Wasserstein Gradient Flow

November 29, 2023

Entropy and the Kullback-Leibler Divergence for Bayesian Networks: Computational Complexity and Efficient Implementation
Marco Scutari
Causal Inference Bayesian Network Kullback Leibler Divergence Computational Complexity Distributional Assumption Stable Entropy Graphical Structure Gaussian Bayesian Network

November 28, 2023

FedECA: A Federated External Control Arm Method for Causal Inference with Time-To-Event Data in Distributed Settings
Jean Ogier du Terrail, Quentin Klopfenstein, Honghao Li, Imke Mayer, Nicolas Loiseau, Mohammad Hallal, Michael Debouver, Thibault Camalon, Thibault Fouqueray, Jorge Arellano Castro, Zahia Yanes, Laetitia Dahan, Julien Taïeb, Pierre Laurent-Puig, Jean-Baptiste Bachet, Shulin Zhao, Remy Nicolle, Jérome Cros, Daniel Gonzalez, Robert Carreras-Torres, Adelaida Garcia Velasco, Kawther Abdilleh, Sudheer Doss, Félix Balazard, Mathieu Andreux
Causal Inference Data Sharing Privacy Enhancing Technology Time to Event Data

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 Causal Model Medical Diagnosis 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

Structured Neural Networks for Density Estimation and Causal Inference
Asic Q. Chen, Ruian Shi, Xiang Gao, Ricardo Baptista, Rahul G. Krishnan
Generative Model Causal Inference Normalizing Flow Density Estimation Autoregressive Flow Efficient Generative Structured Neural Network

Causal Inference

Papers

Instrumental Variable Estimation for Causal Inference in Longitudinal Data with Time-Dependent Latent Confounders

Learning the Causal Structure of Networked Dynamical Systems under Latent Nodes and Structured Noise

Disentangled Latent Representation Learning for Tackling the Confounding M-Bias Problem in Causal Inference

CLadder: Assessing Causal Reasoning in Language Models

Causality and Explainability for Trustworthy Integrated Pest Management

Marginal Density Ratio for Off-Policy Evaluation in Contextual Bandits

SpaCE: The Spatial Confounding Environment

Geometry-Aware Normalizing Wasserstein Flows for Optimal Causal Inference

Entropy and the Kullback-Leibler Divergence for Bayesian Networks: Computational Complexity and Efficient Implementation

FedECA: A Federated External Control Arm Method for Causal Inference with Time-To-Event Data in Distributed Settings

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