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 30, 2022

Evaluating Digital Agriculture Recommendations with Causal Inference
Ilias Tsoumas, Georgios Giannarakis, Vasileios Sitokonstantinou, Alkiviadis Koukos, Dimitra Loka, Nikolaos Bartsotas, Charalampos Kontoes, Ioannis Athanasiadis
Causal Inference Crop Breeding Digital Agriculture

November 29, 2022

Causal Inference with Conditional Instruments using Deep Generative Models
Debo Cheng, Ziqi Xu, Jiuyong Li, Lin Liu, Jixue Liu, Thuc Duy Le
Causal Inference Instrumental Variable Latent Confounders Conditional Expectation Average Causal Effect Treatment Variable

November 28, 2022

Causal Deep Reinforcement Learning Using Observational Data
Wenxuan Zhu, Chao Yu, Qiang Zhang
Deep Reinforcement Learning Causal Inference Deep Q Learning Observational Data Model Free Deep Reinforcement Learning Unobserved Variable Causal Reinforcement Learning

November 21, 2022

Applications of statistical causal inference in software engineering
Julien Siebert
Financial Application Causal Inference Causal Effect Software Engineering Observational Data Research Community

November 19, 2022

Explainable Artificial Intelligence and Causal Inference based ATM Fraud Detection
Yelleti Vivek, Vadlamani Ravi, Abhay Anand Mane, Laveti Ramesh Naidu
Causal Inference Explainable Artificial Intelligence Fraud Detection Fraud Pattern

November 8, 2022

NESTER: An Adaptive Neurosymbolic Method for Causal Effect Estimation
Abbavaram Gowtham Reddy, Vineeth N Balasubramanian
Causal Inference Causal Effect Causal Effect Estimation Neurosymbolic Approach

November 6, 2022

Evaluating Digital Tools for Sustainable Agriculture using Causal Inference
Ilias Tsoumas, Georgios Giannarakis, Vasileios Sitokonstantinou, Alkiviadis Koukos, Dimitra Loka, Nikolaos Bartsotas, Charalampos Kontoes, Ioannis Athanasiadis
Causal Inference Digital Tool Digital Agriculture Observational Causal

November 3, 2022

Empirical Analysis of Model Selection for Heterogeneous Causal Effect Estimation
Divyat Mahajan, Ioannis Mitliagkas, Brady Neal, Vasilis Syrgkanis
Causal Inference Surrogate Model Model Selection Empirical Analysis Conditional Average Treatment Effect Heterogeneous Causal

October 31, 2022

CausalBench: A Large-scale Benchmark for Network Inference from Single-cell Perturbation Data
Mathieu Chevalley, Yusuf Roohani, Arash Mehrjou, Jure Leskovec, Patrick Schwab
Causal Inference Large Scale Benchmark Network Inference CausalBench Challenge

October 30, 2022

Formalizing Statistical Causality via Modal Logic
Yusuke Kawamoto, Tetsuya Sato, Kohei Suenaga
Causal Inference Causal Approach Modal Logic

October 29, 2022

October 28, 2022

A Survey on Causal Representation Learning and Future Work for Medical Image Analysis
Changjie Lu
Timely Survey Transfer Learning Causal Inference Medical Image Analysis Causal Representation Learning Visual Understanding Feature Mining Future Work

October 27, 2022

Sample-Specific Root Causal Inference with Latent Variables
Eric V. Strobl, Thomas A. Lasko
Causal Inference Causal Graph Latent Variable Root Cause Causal Analysis

October 25, 2022

Dynamic Survival Transformers for Causal Inference with Electronic Health Records
Prayag Chatha, Yixin Wang, Zhenke Wu, Jeffrey Regier
Causal Inference Electronic Health Record Deep Survival Model Synthetic EHR

October 18, 2022

October 17, 2022

Causal Inference for De-biasing Motion Estimation from Robotic Observational Data
Junhong Xu, Kai Yin, Jason M. Gregory, Lantao Liu
Causal Inference Motion Estimation Robot Data Inverse Propensity Weighting

October 15, 2022

Fair Effect Attribution in Parallel Online Experiments
Alexander Buchholz, Vito Bellini, Giuseppe Di Benedetto, Yannik Stein, Matteo Ruffini, Fabian Moerchen
Synthetic Data Causal Inference Conditional Average Treatment Effect Online Experiment Interaction Effect

October 12, 2022

Sample Constrained Treatment Effect Estimation
Raghavendra Addanki, David Arbour, Tung Mai, Cameron Musco, Anup Rao
Causal Inference Treatment Effect Experimental Design