Sample, estimate, aggregate: A recipe for causal discovery foundation models

• URL: http://arxiv.org/abs/2402.01929v2
• Date: Thu, 23 May 2024 13:09:20 GMT
• Title: Sample, estimate, aggregate: A recipe for causal discovery foundation models
• Authors: Menghua Wu, Yujia Bao, Regina Barzilay, Tommi Jaakkola,
• Abstract summary: We train a supervised model that learns to predict a larger causal graph from the outputs of classical causal discovery algorithms run over subsets of variables. Our approach is enabled by the observation that typical errors in the outputs of classical methods remain comparable across datasets. Experiments on real and synthetic data demonstrate that this model maintains high accuracy in the face of misspecification or distribution shift.
• Score: 28.116832159265964
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Causal discovery, the task of inferring causal structure from data, promises to accelerate scientific research, inform policy making, and more. However, causal discovery algorithms over larger sets of variables tend to be brittle against misspecification or when data are limited. To mitigate these challenges, we train a supervised model that learns to predict a larger causal graph from the outputs of classical causal discovery algorithms run over subsets of variables, along with other statistical hints like inverse covariance. Our approach is enabled by the observation that typical errors in the outputs of classical methods remain comparable across datasets. Theoretically, we show that this model is well-specified, in the sense that it can recover a causal graph consistent with graphs over subsets. Empirically, we train the model to be robust to erroneous estimates using diverse synthetic data. Experiments on real and synthetic data demonstrate that this model maintains high accuracy in the face of misspecification or distribution shift, and can be adapted at low cost to different discovery algorithms or choice of statistics.

Related papers

• Estimating Causal Effects from Learned Causal Networks [56.14597641617531]
  We propose an alternative paradigm for answering causal-effect queries over discrete observable variables. We learn the causal Bayesian network and its confounding latent variables directly from the observational data. We show that this emphmodel completion learning approach can be more effective than estimand approaches.
  arXiv  Detail & Related papers  (2024-08-26T08:39:09Z)
• Demystifying amortized causal discovery with transformers [21.058343547918053]
  Supervised learning approaches for causal discovery from observational data often achieve competitive performance. In this work, we investigate CSIvA, a transformer-based model promising to train on synthetic data and transfer to real data. We bridge the gap with existing identifiability theory and show that constraints on the training data distribution implicitly define a prior on the test observations.
  arXiv  Detail & Related papers  (2024-05-27T08:17:49Z)
• Discovering Mixtures of Structural Causal Models from Time Series Data [23.18511951330646]
  We propose a general variational inference-based framework called MCD to infer the underlying causal models. Our approach employs an end-to-end training process that maximizes an evidence-lower bound for the data likelihood. We demonstrate that our method surpasses state-of-the-art benchmarks in causal discovery tasks.
  arXiv  Detail & Related papers  (2023-10-10T05:13:10Z)
• Amortized Inference for Causal Structure Learning [72.84105256353801]
  Learning causal structure poses a search problem that typically involves evaluating structures using a score or independence test. We train a variational inference model to predict the causal structure from observational/interventional data. Our models exhibit robust generalization capabilities under substantial distribution shift.
  arXiv  Detail & Related papers  (2022-05-25T17:37:08Z)
• Estimation of Bivariate Structural Causal Models by Variational Gaussian Process Regression Under Likelihoods Parametrised by Normalising Flows [74.85071867225533]
  Causal mechanisms can be described by structural causal models. One major drawback of state-of-the-art artificial intelligence is its lack of explainability.
  arXiv  Detail & Related papers  (2021-09-06T14:52:58Z)
• Improving Efficiency and Accuracy of Causal Discovery Using a Hierarchical Wrapper [7.570246812206772]
  Causal discovery from observational data is an important tool in many branches of science. In the large sample limit, sound and complete causal discovery algorithms have been previously introduced. However, only finite training data is available, which limits the power of statistical tests used by these algorithms.
  arXiv  Detail & Related papers  (2021-07-11T09:24:49Z)
• Identification of Latent Variables From Graphical Model Residuals [0.0]
  We present a novel method to control for the latent space when estimating a DAG by iteratively deriving proxies for the latent space from the residuals of the inferred model. We show that any improvement of prediction of an outcome is intrinsically capped and cannot rise beyond a certain limit as compared to the confounded model.
  arXiv  Detail & Related papers  (2021-01-07T02:28:49Z)
• Good Classifiers are Abundant in the Interpolating Regime [64.72044662855612]
  We develop a methodology to compute precisely the full distribution of test errors among interpolating classifiers. We find that test errors tend to concentrate around a small typical value $varepsilon*$, which deviates substantially from the test error of worst-case interpolating model. Our results show that the usual style of analysis in statistical learning theory may not be fine-grained enough to capture the good generalization performance observed in practice.
  arXiv  Detail & Related papers  (2020-06-22T21:12:31Z)
• Amortized Causal Discovery: Learning to Infer Causal Graphs from Time-Series Data [63.15776078733762]
  We propose Amortized Causal Discovery, a novel framework to learn to infer causal relations from time-series data. We demonstrate experimentally that this approach, implemented as a variational model, leads to significant improvements in causal discovery performance.
  arXiv  Detail & Related papers  (2020-06-18T19:59:12Z)
• Learning Causal Models Online [103.87959747047158]
  Predictive models can rely on spurious correlations in the data for making predictions. One solution for achieving strong generalization is to incorporate causal structures in the models. We propose an online algorithm that continually detects and removes spurious features.
  arXiv  Detail & Related papers  (2020-06-12T20:49:20Z)

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.