66 research outputs found
C-Disentanglement: Discovering Causally-Independent Generative Factors under an Inductive Bias of Confounder
Representation learning assumes that real-world data is generated by a few
semantically meaningful generative factors (i.e., sources of variation) and
aims to discover them in the latent space. These factors are expected to be
causally disentangled, meaning that distinct factors are encoded into separate
latent variables, and changes in one factor will not affect the values of the
others. Compared to statistical independence, causal disentanglement allows
more controllable data generation, improved robustness, and better
generalization. However, most existing work assumes unconfoundedness in the
discovery process, that there are no common causes to the generative factors
and thus obtain only statistical independence. In this paper, we recognize the
importance of modeling confounders in discovering causal generative factors.
Unfortunately, such factors are not identifiable without proper inductive bias.
We fill the gap by introducing a framework entitled Confounded-Disentanglement
(C-Disentanglement), the first framework that explicitly introduces the
inductive bias of confounder via labels from domain expertise. In addition, we
accordingly propose an approach to sufficiently identify the causally
disentangled factors under any inductive bias of the confounder. We conduct
extensive experiments on both synthetic and real-world datasets. Our method
demonstrates competitive results compared to various SOTA baselines in
obtaining causally disentangled features and downstream tasks under domain
shifts.Comment: accepted to Neurips 202
Identification and Estimation of Causal Effects Using non-Gaussianity and Auxiliary Covariates
Assessing causal effects in the presence of unmeasured confounding is a
challenging problem. Although auxiliary variables, such as instrumental
variables, are commonly used to identify causal effects, they are often
unavailable in practice due to stringent and untestable conditions. To address
this issue, previous researches have utilized linear structural equation models
to show that the causal effect can be identifiable when noise variables of the
treatment and outcome are both non-Gaussian. In this paper, we investigate the
problem of identifying the causal effect using auxiliary covariates and
non-Gaussianity from the treatment. Our key idea is to characterize the impact
of unmeasured confounders using an observed covariate, assuming they are all
Gaussian. The auxiliary covariate can be an invalid instrument or an invalid
proxy variable. We demonstrate that the causal effect can be identified using
this measured covariate, even when the only source of non-Gaussianity comes
from the treatment. We then extend the identification results to the
multi-treatment setting and provide sufficient conditions for identification.
Based on our identification results, we propose a simple and efficient
procedure for calculating causal effects and show the -consistency of
the proposed estimator. Finally, we evaluate the performance of our estimator
through simulation studies and an application.Comment: 16 papges, 7 Figure
Sample-Specific Root Causal Inference with Latent Variables
Root causal analysis seeks to identify the set of initial perturbations that
induce an unwanted outcome. In prior work, we defined sample-specific root
causes of disease using exogenous error terms that predict a diagnosis in a
structural equation model. We rigorously quantified predictivity using Shapley
values. However, the associated algorithms for inferring root causes assume no
latent confounding. We relax this assumption by permitting confounding among
the predictors. We then introduce a corresponding procedure called Extract
Errors with Latents (EEL) for recovering the error terms up to contamination by
vertices on certain paths under the linear non-Gaussian acyclic model. EEL also
identifies the smallest sets of dependent errors for fast computation of the
Shapley values. The algorithm bypasses the hard problem of estimating the
underlying causal graph in both cases. Experiments highlight the superior
accuracy and robustness of EEL relative to its predecessors
Computational causal discovery: Advantages and assumptions
I would like to congratulate James Woodward for another landmark accomplishment, after publishing his Making things happen: A theory of causal explanation (Woodward, 2003). Making things happen gives an elegant interventionist theory for understanding explanation and causation. The new contribution (Woodward, 2022) relies on that theory and further makes a big step towards empirical inference of causal relations from non-experimental data. In this paper, I will focus on some of the emerging computational methods for finding causal relations from non-experimental data and attempt to complement Woodward's contribution with discussions on 1) how these methods are connected to the interventionist theory of causality, 2) how informative the output of the methods is, including whether they output directed causal graphs and how they deal with confounders (unmeasured common causes of two measured variables), and 3) the assumptions underlying the asymptotic correctness of the output of the methods about causal relations. Different causal discovery methods may rely on different aspects of the joint distribution of the data, and this discussion aims to provide a technical account of the assumptions
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