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

August 10, 2023

A Forecaster's Review of Judea Pearl's Causality: Models, Reasoning and Inference, Second Edition, 2009
Feng Li
Complex Reasoning Scientific Inference Causal Inference Time Series Forecasting Causal Effect Causal Pattern

August 8, 2023

August 7, 2023

Diffusion Model in Causal Inference with Unmeasured Confounders
Tatsuhiro Shimizu
Diffusion Model Causal Inference Unobserved Confounders Causal Intervention Causal Query Counterfactual Distribution

August 4, 2023

Scalable Computation of Causal Bounds
Madhumitha Shridharan, Garud Iyengar
Causal Inference Causal Graph Causal Query Scalable Algorithm Causal Constraint

August 2, 2023

July 31, 2023

July 26, 2023

Learning sources of variability from high-dimensional observational studies
Eric W. Bridgeford, Jaewon Chung, Brian Gilbert, Sambit Panda, Adam Li, Cencheng Shen, Alexandra Badea, Brian Caffo, Joshua T. Vogelstein
High Dimensional Causal Inference Causal Effect Non Pathological Variability Learning Resource

July 18, 2023

Causality-oriented robustness: exploiting general additive interventions
Xinwei Shen, Peter Bühlmann, Armeen Taeb
Causal Inference Robust Optimization Distributional Robustness Robust Prediction Additive Intervention

July 17, 2023

An R package for parametric estimation of causal effects
Joshua Wolff Anderson, Cyril Rakovski
Causal Inference Causal Effect Causal Model Parameter Estimation R Package

July 11, 2023

Benchmarking Bayesian Causal Discovery Methods for Downstream Treatment Effect Estimation
Chris Chinenye Emezue, Alexandre Drouin, Tristan Deleu, Stefan Bauer, Yoshua Bengio
Causal Inference Causal Discovery Treatment Effect Causal Discovery Method Bayesian Causal Discovery

June 25, 2023

June 24, 2023

Estimating the Causal Effect of Early ArXiving on Paper Acceptance
Yanai Elazar, Jiayao Zhang, David Wadden, Bo Zhang, Noah A. Smith
Causal Inference Causal Effect Peer Review Text Based Confounders Paper Submission

June 21, 2023

Learning Conditional Instrumental Variable Representation for Causal Effect Estimation
Debo Cheng, Ziqi Xu, Jiuyong Li, Lin Liu, Thuc Duy Le, Jixue Liu
Causal Inference Causal Effect Causal Effect Estimation Instrumental Variable Latent Confounders

June 19, 2023

Preserving Commonsense Knowledge from Pre-trained Language Models via Causal Inference
Junhao Zheng, Qianli Ma, Shengjie Qiu, Yue Wu, Peitian Ma, Junlong Liu, Huawen Feng, Xichen Shang, Haibin Chen
Fine Tuning Pre Trained Language Model Causal Inference Commonsense Knowledge

June 18, 2023

Causal Inference

Papers

A Forecaster's Review of Judea Pearl's Causality: Models, Reasoning and Inference, Second Edition, 2009

SLEM: Machine Learning for Path Modeling and Causal Inference with Super Learner Equation Modeling

Generalization bound for estimating causal effects from observational network data

Diffusion Model in Causal Inference with Unmeasured Confounders

Scalable Computation of Causal Bounds

Causal Inference with Differentially Private (Clustered) Outcomes

VLUCI: Variational Learning of Unobserved Confounders for Counterfactual Inference

A continuous Structural Intervention Distance to compare Causal Graphs

Causal Inference for Banking Finance and Insurance A Survey

Learning sources of variability from high-dimensional observational studies

Causality-oriented robustness: exploiting general additive interventions

An R package for parametric estimation of causal effects

Benchmarking Bayesian Causal Discovery Methods for Downstream Treatment Effect Estimation

Comparing Causal Frameworks: Potential Outcomes, Structural Models, Graphs, and Abstractions

Towards Trustworthy Explanation: On Causal Rationalization

Estimating the Causal Effect of Early ArXiving on Paper Acceptance

Learning Conditional Instrumental Variable Representation for Causal Effect Estimation

Preserving Commonsense Knowledge from Pre-trained Language Models via Causal Inference

Identifiable causal inference with noisy treatment and no side information

Can predictive models be used for causal inference?