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

September 21, 2023

Human-in-the-Loop Causal Discovery under Latent Confounding using Ancestral GFlowNets
Tiago da Silva, Eliezer Silva, Adèle Ribeiro, António Góis, Dominik Heider, Samuel Kaski, Diego Mesquita
Causal Inference Causal Discovery Causal Relation Energy Based GFlowNets Latent Confounding Causal Attribution Ancestral Graph

September 19, 2023

Partially Specified Causal Simulations
A. Zamanian, L. Mareis, N. Ahmidi
Causal Inference Novel Causal Model Causal Graphical Model

September 18, 2023

Causal Discovery and Counterfactual Explanations for Personalized Student Learning
Bevan I. Smith
Causal Inference Causal Discovery Counterfactual Explanation Causal Discovery Method Personalized Learning

September 13, 2023

MCNS: Mining Causal Natural Structures Inside Time Series via A Novel Internal Causality Scheme
Yuanhao Liu, Dehui Du, Zihan Jiang, Anyan Huang, Yiyang Li
Causal Inference Causal Graph Causal Structure Causal Structure Learning Causal Approach

September 11, 2023

Using causal inference to avoid fallouts in data-driven parametric analysis: a case study in the architecture, engineering, and construction industry
Xia Chen, Ruiji Sun, Ueli Saluz, Stefano Schiavon, Philipp Geyer
Case Study Data Driven Causal Inference Architecture Design Causal Analysis Data Driven Model Position Engineering Data Driven Modeling

September 8, 2023

Inferring physical laws by artificial intelligence based causal models
Jorawar Singh, Kishor Bharti, Arvind
Machine Learning Artificial Intelligence Causal Inference Causal Model Artificial General Intelligence Cause Effect Physical Law

September 7, 2023

A Causal Perspective on Loan Pricing: Investigating the Impacts of Selection Bias on Identifying Bid-Response Functions
Christopher Bockel-Rickermann, Sam Verboven, Tim Verdonck, Wouter Verbeke
Causal Inference Impact Assessment Selection Bias Global Endogenous Variate Personalized Pricing

September 5, 2023

September 4, 2023

Measuring, Interpreting, and Improving Fairness of Algorithms using Causal Inference and Randomized Experiments
James Enouen, Tianshu Sun, Yan Liu
Practical Algorithm Procedural Fairness Causal Inference Algorithmic Bias Measurement System Algorithmic Decision Fair Algorithm Randomized Experiment

August 24, 2023

August 23, 2023

Benchmarking Causal Study to Interpret Large Language Models for Source Code
Daniel Rodriguez-Cardenas, David N. Palacio, Dipin Khati, Henry Burke, Denys Poshyvanyk
Code Generation Causal Inference Causal Discovery Source Code Code Summarization

August 22, 2023

Does Misclassifying Non-confounding Covariates as Confounders Affect the Causal Inference within the Potential Outcomes Framework?
Yonghe Zhao, Qiang Huang, Shuai Fu, Huiyan Sun
Causal Inference Text Based Confounders Confounding Bias Non Confounding Covariates

August 18, 2023

Active and Passive Causal Inference Learning
Daniel Jiwoong Im, Kyunghyun Cho
Causal Inference Causal Learning Causal Effect Identification Inverse Probability Weighting

August 17, 2023

Uplift Modeling: from Causal Inference to Personalization
Felipe Moraes, Hugo Manuel Proença, Anastasiia Kornilova, Javier Albert, Dmitri Goldenberg
Causal Inference Causal Effect Causal Pattern Uplift Modeling

August 16, 2023

Stable and Causal Inference for Discriminative Self-supervised Deep Visual Representations
Yuewei Yang, Hai Li, Yiran Chen
Representation Learning Real World Causal Inference Self Supervised Visual Representation Ground Truth Causal

August 11, 2023

Causal Inference

Papers

Human-in-the-Loop Causal Discovery under Latent Confounding using Ancestral GFlowNets

Partially Specified Causal Simulations

Causal Discovery and Counterfactual Explanations for Personalized Student Learning

MCNS: Mining Causal Natural Structures Inside Time Series via A Novel Internal Causality Scheme

Using causal inference to avoid fallouts in data-driven parametric analysis: a case study in the architecture, engineering, and construction industry

Inferring physical laws by artificial intelligence based causal models

A Causal Perspective on Loan Pricing: Investigating the Impacts of Selection Bias on Identifying Bid-Response Functions

Causal Structure Recovery of Linear Dynamical Systems: An FFT based Approach

s-ID: Causal Effect Identification in a Sub-Population

Measuring, Interpreting, and Improving Fairness of Algorithms using Causal Inference and Randomized Experiments

Causal Parrots: Large Language Models May Talk Causality But Are Not Causal

Federated Causal Inference from Observational Data

Machine Unlearning for Causal Inference

Benchmarking Causal Study to Interpret Large Language Models for Source Code

Does Misclassifying Non-confounding Covariates as Confounders Affect the Causal Inference within the Potential Outcomes Framework?

Active and Passive Causal Inference Learning

Uplift Modeling: from Causal Inference to Personalization

Stable and Causal Inference for Discriminative Self-supervised Deep Visual Representations

Learning Bayesian Networks with Heterogeneous Agronomic Data Sets via Mixed-Effect Models and Hierarchical Clustering

Towards a Causal Probabilistic Framework for Prediction, Action-Selection & Explanations for Robot Block-Stacking Tasks