14 research outputs found
Causal Discovery in Physical Systems from Videos
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 inferred graph. We assume access to different configurations and environmental conditions, i.e., data from unknown interventions on the underlying system; thus, we can hope to discover the correct underlying causal graph without explicit interventions. We evaluate our method in a planar multi-body interaction environment and scenarios involving fabrics of different shapes like shirts and pants. Experiments demonstrate that our model can correctly identify the interactions from a short sequence of images and make long-term future predictions. The causal structure assumed by the model also allows it to make counterfactual predictions and extrapolate to systems of unseen interaction graphs or graphs of various sizes
Causal Discovery in Physical Systems from Videos
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 inferred graph. We
assume access to different configurations and environmental conditions, i.e.,
data from unknown interventions on the underlying system; thus, we can hope to
discover the correct underlying causal graph without explicit interventions. We
evaluate our method in a planar multi-body interaction environment and
scenarios involving fabrics of different shapes like shirts and pants.
Experiments demonstrate that our model can correctly identify the interactions
from a short sequence of images and make long-term future predictions. The
causal structure assumed by the model also allows it to make counterfactual
predictions and extrapolate to systems of unseen interaction graphs or graphs
of various sizes.Comment: NeurIPS 2020. Project page: https://yunzhuli.github.io/V-CDN
Realization of Causal Representation Learning and Redefined DAG for Causal AI
DAG(Directed Acyclic Graph) from causal inference does not differentiate
causal effects and correlated changes. And the general effect of a population
is usually approximated by averaging correlations over all individuals. Since
AI(Artificial Intelligence) enables large-scale structure modeling on big data,
the complex hidden confoundings have made these approximation errors no longer
ignorable but snowballed to considerable modeling bias - Such Causal
Representation Bias (CRB) leads to many problems: ungeneralizable causal
models, unrevealed individual-level features, hardly utilized causal knowledge
in DL(Deep Learning), etc. In short, DAG must be redefined to enable a new
framework for causal AI.
The observational time series in statistics can only represent correlated
changes, while the DL-based autoencoder can represent them as individualized
feature changes in latent space to estimate the causal effects directly. In
this paper, we introduce the redefined do-DAG to visualize CRB, propose a
generic solution Causal Representation Learning (CRL) framework, along with a
novel architecture for its realization, and experimentally verify the
feasibility
Driver-centric Risk Object Identification
A massive number of traffic fatalities are due to driver errors. To reduce
fatalities, developing intelligent driving systems assisting drivers to
identify potential risks is in urgent need. Risky situations are generally
defined based on collision prediction in existing research. However, collisions
are only one type of risk in traffic scenarios. We believe a more generic
definition is required. In this work, we propose a novel driver-centric
definition of risk, i.e., risky objects influence driver behavior. Based on
this definition, a new task called risk object identification is introduced. We
formulate the task as a cause-effect problem and present a novel two-stage risk
object identification framework, taking inspiration from models of situation
awareness and causal inference. A driver-centric Risk Object Identification
(ROI) dataset is curated to evaluate the proposed system. We demonstrate
state-of-the-art risk object identification performance compared with strong
baselines on the ROI dataset. In addition, we conduct extensive ablative
studies to justify our design choices.Comment: Submitted to TPAM
Aligning Robot and Human Representations
To act in the world, robots rely on a representation of salient task aspects:
for example, to carry a coffee mug, a robot may consider movement efficiency or
mug orientation in its behavior. However, if we want robots to act for and with
people, their representations must not be just functional but also reflective
of what humans care about, i.e. they must be aligned. We observe that current
learning approaches suffer from representation misalignment, where the robot's
learned representation does not capture the human's representation. We suggest
that because humans are the ultimate evaluator of robot performance, we must
explicitly focus our efforts on aligning learned representations with humans,
in addition to learning the downstream task. We advocate that current
representation learning approaches in robotics should be studied from the
perspective of how well they accomplish the objective of representation
alignment. We mathematically define the problem, identify its key desiderata,
and situate current methods within this formalism. We conclude by suggesting
future directions for exploring open challenges.Comment: 14 pages, 3 figures, 1 tabl
Exploring Causal Learning through Graph Neural Networks: An In-depth Review
In machine learning, exploring data correlations to predict outcomes is a
fundamental task. Recognizing causal relationships embedded within data is
pivotal for a comprehensive understanding of system dynamics, the significance
of which is paramount in data-driven decision-making processes. Beyond
traditional methods, there has been a surge in the use of graph neural networks
(GNNs) for causal learning, given their capabilities as universal data
approximators. Thus, a thorough review of the advancements in causal learning
using GNNs is both relevant and timely. To structure this review, we introduce
a novel taxonomy that encompasses various state-of-the-art GNN methods employed
in studying causality. GNNs are further categorized based on their applications
in the causality domain. We further provide an exhaustive compilation of
datasets integral to causal learning with GNNs to serve as a resource for
practical study. This review also touches upon the application of causal
learning across diverse sectors. We conclude the review with insights into
potential challenges and promising avenues for future exploration in this
rapidly evolving field of machine learning
Deep Causal Learning: Representation, Discovery and Inference
Causal learning has attracted much attention in recent years because
causality reveals the essential relationship between things and indicates how
the world progresses. However, there are many problems and bottlenecks in
traditional causal learning methods, such as high-dimensional unstructured
variables, combinatorial optimization problems, unknown intervention,
unobserved confounders, selection bias and estimation bias. Deep causal
learning, that is, causal learning based on deep neural networks, brings new
insights for addressing these problems. While many deep learning-based causal
discovery and causal inference methods have been proposed, there is a lack of
reviews exploring the internal mechanism of deep learning to improve causal
learning. In this article, we comprehensively review how deep learning can
contribute to causal learning by addressing conventional challenges from three
aspects: representation, discovery, and inference. We point out that deep
causal learning is important for the theoretical extension and application
expansion of causal science and is also an indispensable part of general
artificial intelligence. We conclude the article with a summary of open issues
and potential directions for future work
D'ya like DAGs? A Survey on Structure Learning and Causal Discovery
Causal reasoning is a crucial part of science and human intelligence. In
order to discover causal relationships from data, we need structure discovery
methods. We provide a review of background theory and a survey of methods for
structure discovery. We primarily focus on modern, continuous optimization
methods, and provide reference to further resources such as benchmark datasets
and software packages. Finally, we discuss the assumptive leap required to take
us from structure to causality.Comment: 35 page