Causal Discovery in Physical Systems from Videos
Yunzhu Li, Antonio Torralba, Animashree Anandkumar, Dieter Fox,, Animesh Garg

TL;DR
This paper presents an end-to-end method for discovering causal relationships in physical systems from videos without supervision, enabling understanding, prediction, and counterfactual reasoning of complex interactions.
Contribution
It introduces a novel model combining perception, inference, and dynamics modules to infer causal graphs directly from video data without explicit supervision.
Findings
Successfully identifies interactions in multi-body systems from short video sequences.
Accurately predicts future states of physical systems based on inferred causal structures.
Enables counterfactual reasoning and extrapolation to unseen interaction graphs.
Abstract
Causal discovery is at the core of human cognition. It enables us to reason about the environment and make counterfactual predictions about unseen scenarios that can vastly differ from our previous experiences. We consider the task of causal discovery from videos in an end-to-end fashion without supervision on the ground-truth graph structure. In particular, our goal is to discover the structural dependencies among environmental and object variables: inferring the type and strength of interactions that have a causal effect on the behavior of the dynamical system. Our model consists of (a) a perception module that extracts a semantically meaningful and temporally consistent keypoint representation from images, (b) an inference module for determining the graph distribution induced by the detected keypoints, and (c) a dynamics module that can predict the future by conditioning on the…
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Code & Models
Videos
Taxonomy
TopicsExplainable Artificial Intelligence (XAI) · Anomaly Detection Techniques and Applications · Gaussian Processes and Bayesian Inference
