Causal Effect Identification

Causal effect identification focuses on determining the causal relationships between variables, aiming to establish whether changes in one variable directly cause changes in another, rather than simply observing correlations. Current research emphasizes developing efficient algorithms and model architectures, such as those based on transformers and deep learning, to identify causal effects from observational data, often incorporating background knowledge or proxy experiments to address challenges like confounding and high dimensionality. This field is crucial for advancing scientific understanding across diverse disciplines and informing evidence-based decision-making in areas such as healthcare, economics, and policy, by enabling more reliable causal inference from complex datasets.

Papers

September 4, 2024

Causality-Aware Transformer Networks for Robotic Navigation
Ruoyu Wang, Yao Liu, Yuanjiang Cao, Lina Yao
Robot Navigation Embodied AI Causal Transformer Causal Effect Identification Embodied Task

July 21, 2024

New Rules for Causal Identification with Background Knowledge
Tian-Zuo Wang, Lue Tao, Zhi-Hua Zhou
Causal Effect Latent Variable Causal Relation Simple RULE Background Knowledge Causal Effect Identification

July 7, 2024

Fast Proxy Experiment Design for Causal Effect Identification
Sepehr Elahi, Sina Akbari, Jalal Etesami, Negar Kiyavash, Patrick Thiran
Causal Effect Causal Effect Identification Proxy Metric Unmeasured Confounding

June 29, 2024

Deciphering interventional dynamical causality from non-intervention systems
Jifan Shi, Yang Li, Juan Zhao, Siyang Leng, Kazuyuki Aihara, Luonan Chen, Wei Lin
Causal Pattern Dynamical Model Causal Effect Identification Causal System Causal Entropy

June 4, 2024

Causal Effect Identification in LiNGAM Models with Latent Confounders
Daniele Tramontano, Yaroslav Kivva, Saber Salehkaleybar, Mathias Drton, Negar Kiyavash
Causal Graph Causal Effect Latent Variable Latent Confounders Causal Effect Identification Parameter Identifiability Linear Non Gaussian Acyclic Model

May 31, 2024

Identifying while Learning for Document Event Causality Identification
Cheng Liu, Wei Xiang, Bang Wang
LeArning Abstract Causal Graph Causal Structure Learning Event Causality Identification Causal Effect Identification

May 23, 2024

Causal Effect Identification in a Sub-Population with Latent Variables
Amir Mohammad Abouei, Ehsan Mokhtarian, Negar Kiyavash, Matthias Grossglauser
Causal Effect Latent Variable Observational Data Causal Effect Identification ID Problem Graphical Structure Sub Population

May 11, 2024

Causal Inference from Slowly Varying Nonstationary Processes
Kang Du, Yu Xiang
Time Series Causal Inference Structural Causal Model Non Stationarity Causal Effect Identification

March 14, 2024

Algorithmic syntactic causal identification
Dhurim Cakiqi, Max A. Little
Causal Effect Identification Causal Bayesian Network

February 1, 2024

DoubleMLDeep: Estimation of Causal Effects with Multimodal Data
Sven Klaassen, Jan Teichert-Kluge, Philipp Bach, Victor Chernozhukov, Martin Spindler, Suhas Vijaykumar
Causal Inference Estimation Task Multimodal Data Causal Effect Treatment Effect Causal Effect Estimation Causal Effect Identification Double Machine Learning

December 28, 2023

LLM4Causal: Democratized Causal Tools for Everyone via Large Language Model
Haitao Jiang, Lin Ge, Yuhe Gao, Jianian Wang, Rui Song
Large Language Model Decision Theory Causal Effect Identification Causal Interpretation

September 22, 2023

OpportunityFinder: A Framework for Automated Causal Inference
Huy Nguyen, Prince Grover, Devashish Khatwani
New Framework Causal Inference Emerging Opportunity Causal Effect Causal Analysis Causal Effect Identification

September 5, 2023

s-ID: Causal Effect Identification in a Sub-Population
Amir Mohammad Abouei, Ehsan Mokhtarian, Negar Kiyavash
Causal Inference Causal Graph Causal Effect Inference Problem Causal Effect Identification ID Problem Sub Population

August 18, 2023

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

July 27, 2023

RCT Rejection Sampling for Causal Estimation Evaluation
Katherine A. Keith, Sergey Feldman, David Jurgens, Jonathan Bragg, Rohit Bhattacharya
Causal Effect Estimation Causal Effect Identification Confounding Bias Unmeasured Confounding Rejection Sampling

June 1, 2023

CAISA at SemEval-2023 Task 8: Counterfactual Data Augmentation for Mitigating Class Imbalance in Causal Claim Identification
Akbar Karimi, Lucie Flek
Machine Learning Model Class Imbalance SemEval 2022 Task Data Augmentation Technique Early Slavic Participle Counterfactual Data Augmentation Causal Effect Identification

February 28, 2023

Representation Disentaglement via Regularization by Causal Identification
Juan Castorena
Generative Model Disentanglement Capability Generative Learning Causal Effect Identification Representation Disentanglement Disentanglement Constraint

January 29, 2023

Emerging Synergies in Causality and Deep Generative Models: A Survey
Guanglin Zhou, Shaoan Xie, Guangyuan Hao, Shiming Chen, Biwei Huang, Xiwei Xu, Chen Wang, Liming Zhu, Lina Yao, Kun Zhang
Timely Survey Causal Pattern Causal Effect Identification Large Scale Generative Model Emerging Synergy

November 22, 2022

Variation-based Cause Effect Identification
Mohamed Amine ben Salem, Karim Said Barsim, Bin Yang
Causal Discovery Relevant Covariates Directed Acyclic Graph Causal Effect Identification

September 30, 2022

Causal Estimation for Text Data with (Apparent) Overlap Violations
Lin Gui, Victor Veitch
Causal Effect Causal Effect Estimation Text Data Causal Effect Identification Robust Causal Conditional Mean