Do Finetti: On Causal Effects for Exchangeable Data
- URL: http://arxiv.org/abs/2405.18836v1
- Date: Wed, 29 May 2024 07:31:18 GMT
- Title: Do Finetti: On Causal Effects for Exchangeable Data
- Authors: Siyuan Guo, Chi Zhang, Karthika Mohan, Ferenc Huszár, Bernhard Schölkopf,
- Abstract summary: We study causal effect estimation in a setting where the data are not i.i.d.
We focus on exchangeable data satisfying an assumption of independent causal mechanisms.
- Score: 45.96632286841583
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: We study causal effect estimation in a setting where the data are not i.i.d. (independent and identically distributed). We focus on exchangeable data satisfying an assumption of independent causal mechanisms. Traditional causal effect estimation frameworks, e.g., relying on structural causal models and do-calculus, are typically limited to i.i.d. data and do not extend to more general exchangeable generative processes, which naturally arise in multi-environment data. To address this gap, we develop a generalized framework for exchangeable data and introduce a truncated factorization formula that facilitates both the identification and estimation of causal effects in our setting. To illustrate potential applications, we introduce a causal P\'olya urn model and demonstrate how intervention propagates effects in exchangeable data settings. Finally, we develop an algorithm that performs simultaneous causal discovery and effect estimation given multi-environment data.
Related papers
- DAG-aware Transformer for Causal Effect Estimation [0.8192907805418583]
Causal inference is a critical task across fields such as healthcare, economics, and the social sciences.
In this paper, we present a novel transformer-based method for causal inference that overcomes these challenges.
The core innovation of our model lies in its integration of causal Directed Acyclic Graphs (DAGs) directly into the attention mechanism.
arXiv Detail & Related papers (2024-10-13T23:17:58Z) - Federated Causal Discovery from Heterogeneous Data [70.31070224690399]
We propose a novel FCD method attempting to accommodate arbitrary causal models and heterogeneous data.
These approaches involve constructing summary statistics as a proxy of the raw data to protect data privacy.
We conduct extensive experiments on synthetic and real datasets to show the efficacy of our method.
arXiv Detail & Related papers (2024-02-20T18:53:53Z) - Disentangle Estimation of Causal Effects from Cross-Silo Data [14.684584362172666]
We introduce an innovative disentangle architecture designed to facilitate the seamless cross-silo transmission of model parameters.
We introduce global constraints into the equation to effectively mitigate bias within the various missing domains.
Our method outperforms state-of-the-art baselines.
arXiv Detail & Related papers (2024-01-04T09:05:37Z) - 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) - Advancing Counterfactual Inference through Nonlinear Quantile Regression [77.28323341329461]
We propose a framework for efficient and effective counterfactual inference implemented with neural networks.
The proposed approach enhances the capacity to generalize estimated counterfactual outcomes to unseen data.
Empirical results conducted on multiple datasets offer compelling support for our theoretical assertions.
arXiv Detail & Related papers (2023-06-09T08:30:51Z) - A Causal Framework for Decomposing Spurious Variations [68.12191782657437]
We develop tools for decomposing spurious variations in Markovian and Semi-Markovian models.
We prove the first results that allow a non-parametric decomposition of spurious effects.
The described approach has several applications, ranging from explainable and fair AI to questions in epidemiology and medicine.
arXiv Detail & Related papers (2023-06-08T09:40:28Z) - Federated Estimation of Causal Effects from Observational Data [19.657789891394504]
We present a novel framework for causal inference with federated data sources.
We assess and integrate local causal effects from different private data sources without centralizing them.
arXiv Detail & Related papers (2021-05-31T08:06:00Z) - Multi-Source Causal Inference Using Control Variates [81.57072928775509]
We propose a general algorithm to estimate causal effects from emphmultiple data sources.
We show theoretically that this reduces the variance of the ATE estimate.
We apply this framework to inference from observational data under an outcome selection bias.
arXiv Detail & Related papers (2021-03-30T21:20:51Z) - Efficient Causal Inference from Combined Observational and
Interventional Data through Causal Reductions [68.6505592770171]
Unobserved confounding is one of the main challenges when estimating causal effects.
We propose a novel causal reduction method that replaces an arbitrary number of possibly high-dimensional latent confounders.
We propose a learning algorithm to estimate the parameterized reduced model jointly from observational and interventional data.
arXiv Detail & Related papers (2021-03-08T14:29:07Z) - Estimating Causal Effects with the Neural Autoregressive Density
Estimator [6.59529078336196]
We use neural autoregressive density estimators to estimate causal effects within the Pearl's do-calculus framework.
We show that the approach can retrieve causal effects from non-linear systems without explicitly modeling the interactions between the variables.
arXiv Detail & Related papers (2020-08-17T13:12:38Z)
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.