Causal Query

Causal query research focuses on developing methods to accurately answer questions about cause-and-effect relationships, moving beyond simple correlations to understand underlying mechanisms. Current efforts concentrate on improving the efficiency and accuracy of causal inference algorithms, particularly for high-dimensional data and complex scenarios involving multiple agents or unobserved confounders, often employing Bayesian networks, graphical models, and diffusion models. This work is crucial for advancing scientific discovery across diverse fields, enabling more reliable predictions and informed decision-making in areas such as healthcare, social sciences, and autonomous systems. The development of large datasets of naturally occurring causal questions is also a key area of focus, improving the training and evaluation of causal reasoning models.

Papers

January 5, 2023

Reasoning about Causality in Games
Lewis Hammond, James Fox, Tom Everitt, Ryan Carey, Alessandro Abate, Michael Wooldridge
Causal Pattern Game Theoretic Video Game Causal Reasoning Causal Query

October 27, 2022

Grasping Causality for the Explanation of Criticality for Automated Driving
Tjark Koopmann, Christian Neurohr, Lina Putze, Lukas Westhofen, Roman Gansch, Ahmad Adee
Line by Line Explanation Causal Structure Automated Driving Causal Knowledge Causal Query Causal Interpretation Criticality Ordered Spin Sequence Causality Based Perspective

October 21, 2022

WikiWhy: Answering and Explaining Cause-and-Effect Questions
Matthew Ho, Aditya Sharma, Justin Chang, Michael Saxon, Sharon Levy, Yujie Lu, William Yang Wang
Complex Reasoning Reasoning Capability Causal Query

August 31, 2022

Partial Counterfactual Identification for Infinite Horizon Partially Observable Markov Decision Process
Aditya Kelvianto Sidharta
Q Learning Observable Markov Decision Process Infinite Horizon Causal Query Counterfactual Query

August 19, 2022

Application of Causal Inference to Analytical Customer Relationship Management in Banking and Insurance
Satyam Kumar, Vadlamani Ravi
Application Proficiency High Explainability Causal Inference Causal Pattern Actual Cause Causal Query Insurance Industry

August 9, 2022

Causal Effect Identification in Uncertain Causal Networks
Sina Akbari, Fateme Jamshidi, Ehsan Mokhtarian, Matthew J. Vowels, Jalal Etesami, Negar Kiyavash
Causal Graph Causal Structure Causal Query Causal Effect Identification Probabilistic Dependency

July 26, 2022

Bounding Counterfactuals under Selection Bias
Marco Zaffalon, Alessandro Antonucci, Rafael Cabañas, David Huber, Dario Azzimonti
Causal Effect Estimation Causal Analysis Selection Bias Causal Query Parameter Identifiability Counterfactual Bound

June 14, 2022

DoWhy-GCM: An extension of DoWhy for causal inference in graphical causal models
Patrick Blöbaum, Peter Götz, Kailash Budhathoki, Atalanti A. Mastakouri, Dominik Janzing
Causal Inference Causal Graph Extension Study Causal Query Causal Graphical Model Visual Causality Discovery

May 12, 2022

e-CARE: a New Dataset for Exploring Explainable Causal Reasoning
Li Du, Xiao Ding, Kai Xiong, Ting Liu, Bing Qin
Causal Reasoning Mobile Health Causal Query Causal Machine Explainable Causal

February 2, 2022

Some Reflections on Drawing Causal Inference using Textual Data: Parallels Between Human Subjects and Organized Texts
Bo Zhang, Jiayao Zhang
Causal Inference Text Based Causal Query Parallel Text Human Subject Causal Concept