Interpretable Causal

Interpretable causal inference aims to understand and model causal relationships in data while providing transparent and understandable explanations for the resulting inferences. Current research focuses on developing novel model architectures, such as causal rule forests and causal concept embedding models, and adapting existing methods like decision trees and variational autoencoders to enhance interpretability, often incorporating techniques from causal mediation analysis and Granger causality. This field is crucial for building trust in AI systems, particularly in high-stakes domains like healthcare and policy-making, by enabling the identification of causal mechanisms and the reliable estimation of treatment effects from observational data.

Papers

August 27, 2024

Causal Rule Forest: Toward Interpretable and Precise Treatment Effect Estimation
Chan Hsu, Jun-Ting Wu, Yihuang Kang
Treatment Effect Conditional Average Treatment Effect Interpretable Causal Heterogeneity Challenge Causal Forest

August 2, 2024

The Quest for the Right Mediator: A History, Survey, and Theoretical Grounding of Causal Interpretability
Aaron Mueller, Jannik Brinkmann, Millicent Li, Samuel Marks, Koyena Pal, Nikhil Prakash, Can Rager, Aruna Sankaranarayanan, Arnab Sen Sharma, Jiuding Sun, Eric Todd, David Bau, Yonatan Belinkov
Timely Survey Inherent Interpretability Interpretability Research Causal Mediation Analysis Interpretable Causal QueST Observed Mediator Mechanistic Understanding Good Interpretability

June 24, 2024

CausalMMM: Learning Causal Structure for Marketing Mix Modeling
Chang Gong, Di Yao, Lei Zhang, Sheng Chen, Wenbin Li, Yueyang Su, Jingping Bi
Causal Structure Interpretable Causal Marketing Mix Modeling

May 26, 2024

Causal Concept Graph Models: Beyond Causal Opacity in Deep Learning
Gabriele Dominici, Pietro Barbiero, Mateo Espinosa Zarlenga, Alberto Termine, Martin Gjoreski, Giuseppe Marra, Marc Langheinrich
Deep Learning Full Model Causal Model Ground Glass Opacity Interpretable Causal Causal Concept

April 22, 2024

ICST-DNET: An Interpretable Causal Spatio-Temporal Diffusion Network for Traffic Speed Prediction
Yi Rong, Yingchi Mao, Yinqiu Liu, Ling Chen, Xiaoming He, Dusit Niyato
Interpretable Causal Traffic Speed Speed Map Temporal Spatial Causal

February 19, 2024

CausalGym: Benchmarking causal interpretability methods on linguistic tasks
Aryaman Arora, Dan Jurafsky, Christopher Potts
Language Model Model Interpretability Interpretability Method Psycholinguistic Research Language Task Interpretable Causal

January 31, 2024

Hierarchical Bias-Driven Stratification for Interpretable Causal Effect Estimation
Lucile Ter-Minassian, Liran Szlak, Ehud Karavani, Chris Holmes, Yishai Shimoni
Causal Effect Estimation Balancing Strategy Interpretable Causal Hierarchical Bias Driven Stratification Treatment Selection Bias

December 20, 2023

Task-Driven Causal Feature Distillation: Towards Trustworthy Risk Prediction
Zhixuan Chu, Mengxuan Hu, Qing Cui, Longfei Li, Sheng Li
Interpretable Causal Causal Feature Distillation

December 17, 2023

Interpretable Causal Inference for Analyzing Wearable, Sensor, and Distributional Data
Srikar Katta, Harsh Parikh, Cynthia Rudin, Alexander Volfovsky
Wearable Device Distributional Assumption High Quality Sensor Interpretable Causal Distributional Model

November 30, 2023

Targeted Reduction of Causal Models
Armin Kekić, Bernhard Schölkopf, Michel Besserve
Causal Discovery Causal Representation Learning Constructive Reduction Causal Variable Interpretable Causal Complex Model

October 27, 2023

Causal disentanglement of multimodal data
Elise Walker, Jonas A. Actor, Carianne Martinez, Nathaniel Trask
Multimodal Data Causal Representation Learning Causal Representation Causal Disentanglement Interpretable Causal Causal Learning Algorithm

October 21, 2023

Counterfactual Prediction Under Selective Confounding
Sohaib Kiani, Jared Barton, Jon Sushinsky, Lynda Heimbach, Bo Luo
Causal Inference Counterfactual Prediction Text Based Confounders Interpretable Causal Non Confounding Covariates

September 14, 2023

Causal Entropy and Information Gain for Measuring Causal Control
Francisco Nunes Ferreira Quialheiro Simoes, Mehdi Dastani, Thijs van Ommen
Causal Structure Information Gain Interpretable Causal Causal Entropy Causal Influence

August 18, 2023

CTP:A Causal Interpretable Model for Non-Communicable Disease Progression Prediction
Zhoujian Sun, Wenzhuo Zhang, Zhengxing Huang, Nai Ding, Cheng Luo
Causal Discovery Causal Graph Disease Progression Causal Prediction Interpretable Causal

May 31, 2023

Neuro-Causal Factor Analysis
Alex Markham, Mingyu Liu, Bryon Aragam, Liam Solus
Causal Discovery Latent Factor Interpretable Causal Causal Factor

May 15, 2023

Interpretability at Scale: Identifying Causal Mechanisms in Alpaca
Zhengxuan Wu, Atticus Geiger, Thomas Icard, Christopher Potts, Noah D. Goodman
Large Language Model Language Model Inherent Interpretability Visual Analogue Scale Interpretable Causal Interpretable Algorithm General Purpose Language Model

March 11, 2023

Learning interpretable causal networks from very large datasets, application to 400,000 medical records of breast cancer patients
Marcel da Câmara Ribeiro-Dantas, Honghao Li, Vincent Cabeli, Louise Dupuis, Franck Simon, Liza Hettal, Anne-Sophie Hamy, Hervé Isambert
LeArning Abstract Causal Discovery Clinical Note Breast Cancer Causal Relation Latent Causal Large Datasets Causal Network Interpretable Causal

March 5, 2023

Finding Alignments Between Interpretable Causal Variables and Distributed Neural Representations
Atticus Geiger, Zhengxuan Wu, Christopher Potts, Thomas Icard, Noah D. Goodman
Explainable AI Neural Representation Alignment Performance Causal Abstraction Interpretable Causal

July 11, 2022

CAMS: An Annotated Corpus for Causal Analysis of Mental Health Issues in Social Media Posts
Muskan Garg, Chandni Saxena, Veena Krishnan, Ruchi Joshi, Sriparna Saha, Vijay Mago, Bonnie J Dorr
Social Medium Mental Health Causal Analysis Annotated Corpus Causal Interpretation Interpretable Causal Causal Categorization Meteor Detection