BayesIMP: Uncertainty Quantification for Causal Data Fusion
- URL: http://arxiv.org/abs/2106.03477v1
- Date: Mon, 7 Jun 2021 10:14:18 GMT
- Title: BayesIMP: Uncertainty Quantification for Causal Data Fusion
- Authors: Siu Lun Chau, Jean-Fran\c{c}ois Ton, Javier Gonz\'alez, Yee Whye Teh,
Dino Sejdinovic
- Abstract summary: We study the causal data fusion problem, where datasets pertaining to multiple causal graphs are combined to estimate the average treatment effect of a target variable.
We introduce a framework which combines ideas from probabilistic integration and kernel mean embeddings to represent interventional distributions in the reproducing kernel Hilbert space.
- Score: 52.184885680729224
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: While causal models are becoming one of the mainstays of machine learning,
the problem of uncertainty quantification in causal inference remains
challenging. In this paper, we study the causal data fusion problem, where
datasets pertaining to multiple causal graphs are combined to estimate the
average treatment effect of a target variable. As data arises from multiple
sources and can vary in quality and quantity, principled uncertainty
quantification becomes essential. To that end, we introduce Bayesian
Interventional Mean Processes, a framework which combines ideas from
probabilistic integration and kernel mean embeddings to represent
interventional distributions in the reproducing kernel Hilbert space, while
taking into account the uncertainty within each causal graph. To demonstrate
the utility of our uncertainty estimation, we apply our method to the Causal
Bayesian Optimisation task and show improvements over state-of-the-art methods.
Related papers
- Identifiability Guarantees for Causal Disentanglement from Purely Observational Data [10.482728002416348]
Causal disentanglement aims to learn about latent causal factors behind data.
Recent advances establish identifiability results assuming that interventions on (single) latent factors are available.
We provide a precise characterization of latent factors that can be identified in nonlinear causal models.
arXiv Detail & Related papers (2024-10-31T04:18:29Z) - An Overview of Causal Inference using Kernel Embeddings [14.298666697532838]
Kernel embeddings have emerged as a powerful tool for representing probability measures in a variety of statistical inference problems.
Main challenges include identifying causal associations and estimating the average treatment effect from observational data.
arXiv Detail & Related papers (2024-10-30T07:23:34Z) - Bayesian Causal Inference with Gaussian Process Networks [1.7188280334580197]
We consider the problem of the Bayesian estimation of the effects of hypothetical interventions in the Gaussian Process Network model.
We detail how to perform causal inference on GPNs by simulating the effect of an intervention across the whole network and propagating the effect of the intervention on downstream variables.
We extend both frameworks beyond the case of a known causal graph, incorporating uncertainty about the causal structure via Markov chain Monte Carlo methods.
arXiv Detail & Related papers (2024-02-01T14:39:59Z) - Towards stable real-world equation discovery with assessing
differentiating quality influence [52.2980614912553]
We propose alternatives to the commonly used finite differences-based method.
We evaluate these methods in terms of applicability to problems, similar to the real ones, and their ability to ensure the convergence of equation discovery algorithms.
arXiv Detail & Related papers (2023-11-09T23:32:06Z) - Identifiability Guarantees for Causal Disentanglement from Soft
Interventions [26.435199501882806]
Causal disentanglement aims to uncover a representation of data using latent variables that are interrelated through a causal model.
In this paper, we focus on the scenario where unpaired observational and interventional data are available, with each intervention changing the mechanism of a latent variable.
When the causal variables are fully observed, statistically consistent algorithms have been developed to identify the causal model under faithfulness assumptions.
arXiv Detail & Related papers (2023-07-12T15:39:39Z) - Nonparametric Identifiability of Causal Representations from Unknown
Interventions [63.1354734978244]
We study causal representation learning, the task of inferring latent causal variables and their causal relations from mixtures of the variables.
Our goal is to identify both the ground truth latents and their causal graph up to a set of ambiguities which we show to be irresolvable from interventional data.
arXiv Detail & Related papers (2023-06-01T10:51:58Z) - Variational Causal Networks: Approximate Bayesian Inference over Causal
Structures [132.74509389517203]
We introduce a parametric variational family modelled by an autoregressive distribution over the space of discrete DAGs.
In experiments, we demonstrate that the proposed variational posterior is able to provide a good approximation of the true posterior.
arXiv Detail & Related papers (2021-06-14T17:52:49Z) - Disentangling Observed Causal Effects from Latent Confounders using
Method of Moments [67.27068846108047]
We provide guarantees on identifiability and learnability under mild assumptions.
We develop efficient algorithms based on coupled tensor decomposition with linear constraints to obtain scalable and guaranteed solutions.
arXiv Detail & Related papers (2021-01-17T07:48:45Z) - Latent Causal Invariant Model [128.7508609492542]
Current supervised learning can learn spurious correlation during the data-fitting process.
We propose a Latent Causal Invariance Model (LaCIM) which pursues causal prediction.
arXiv Detail & Related papers (2020-11-04T10:00:27Z) - Uncertainty Quantification for Inferring Hawkes Networks [13.283258096829146]
We develop a statistical inference framework to learn causal relationships between nodes from networked data.
We provide uncertainty quantification for the maximum likelihood estimate of the network Hawkes process.
arXiv Detail & Related papers (2020-06-12T23:08:36Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.