The Causal Neural Connection: Expressiveness, Learnability, and Inference

• URL: http://arxiv.org/abs/2107.00793v1
• Date: Fri, 2 Jul 2021 01:55:18 GMT
• Title: The Causal Neural Connection: Expressiveness, Learnability, and Inference
• Authors: Kevin Xia, Kai-Zhan Lee, Yoshua Bengio, Elias Bareinboim
• Abstract summary: An object called structural causal model (SCM) represents a collection of mechanisms and sources of random variation of the system under investigation. In this paper, we show that the causal hierarchy theorem (Thm. 1, Bareinboim et al., 2020) still holds for neural models. We introduce a special type of SCM called a neural causal model (NCM), and formalize a new type of inductive bias to encode structural constraints necessary for performing causal inferences.
• Score: 125.57815987218756
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: One of the central elements of any causal inference is an object called structural causal model (SCM), which represents a collection of mechanisms and exogenous sources of random variation of the system under investigation (Pearl, 2000). An important property of many kinds of neural networks is universal approximability: the ability to approximate any function to arbitrary precision. Given this property, one may be tempted to surmise that a collection of neural nets is capable of learning any SCM by training on data generated by that SCM. In this paper, we show this is not the case by disentangling the notions of expressivity and learnability. Specifically, we show that the causal hierarchy theorem (Thm. 1, Bareinboim et al., 2020), which describes the limits of what can be learned from data, still holds for neural models. For instance, an arbitrarily complex and expressive neural net is unable to predict the effects of interventions given observational data alone. Given this result, we introduce a special type of SCM called a neural causal model (NCM), and formalize a new type of inductive bias to encode structural constraints necessary for performing causal inferences. Building on this new class of models, we focus on solving two canonical tasks found in the literature known as causal identification and estimation. Leveraging the neural toolbox, we develop an algorithm that is both sufficient and necessary to determine whether a causal effect can be learned from data (i.e., causal identifiability); it then estimates the effect whenever identifiability holds (causal estimation). Simulations corroborate the proposed approach.

Related papers

• Consistency of Neural Causal Partial Identification [17.503562318576414]
  Recent progress in Causal Models showcased how identification and partial identification of causal effects can be automatically carried out via neural generative models. We prove consistency of partial identification via NCMs in a general setting with both continuous and categorical variables. Results highlight the impact of the design of the underlying neural network architecture in terms of depth and connectivity.
  arXiv  Detail & Related papers  (2024-05-24T16:12:39Z)
• Inferring Inference [7.11780383076327]
  We develop a framework for inferring canonical distributed computations from large-scale neural activity patterns. We simulate recordings for a model brain that implicitly implements an approximate inference algorithm on a probabilistic graphical model. Overall, this framework provides a new tool for discovering interpretable structure in neural recordings.
  arXiv  Detail & Related papers  (2023-10-04T22:12:11Z)
• Neural Dependencies Emerging from Learning Massive Categories [94.77992221690742]
  This work presents two astonishing findings on neural networks learned for large-scale image classification. 1) Given a well-trained model, the logits predicted for some category can be directly obtained by linearly combining the predictions of a few other categories. 2) Neural dependencies exist not only within a single model, but even between two independently learned models.
  arXiv  Detail & Related papers  (2022-11-21T09:42:15Z)
• Neural Causal Models for Counterfactual Identification and Estimation [62.30444687707919]
  We study the evaluation of counterfactual statements through neural models. First, we show that neural causal models (NCMs) are expressive enough. Second, we develop an algorithm for simultaneously identifying and estimating counterfactual distributions.
  arXiv  Detail & Related papers  (2022-09-30T18:29:09Z)
• Relating Graph Neural Networks to Structural Causal Models [17.276657786213015]
  Causality can be described in terms of a structural causal model (SCM) that carries information on the variables of interest and their mechanistic relations. We present a theoretical analysis that establishes a novel connection between GNN and SCM. We then establish a new model class for GNN-based causal inference that is necessary and sufficient for causal effect identification.
  arXiv  Detail & Related papers  (2021-09-09T11:16:31Z)
• Learning Neural Causal Models with Active Interventions [83.44636110899742]
  We introduce an active intervention-targeting mechanism which enables a quick identification of the underlying causal structure of the data-generating process. Our method significantly reduces the required number of interactions compared with random intervention targeting. We demonstrate superior performance on multiple benchmarks from simulated to real-world data.
  arXiv  Detail & Related papers  (2021-09-06T13:10:37Z)
• Systematic Evaluation of Causal Discovery in Visual Model Based Reinforcement Learning [76.00395335702572]
  A central goal for AI and causality is the joint discovery of abstract representations and causal structure. Existing environments for studying causal induction are poorly suited for this objective because they have complicated task-specific causal graphs. In this work, our goal is to facilitate research in learning representations of high-level variables as well as causal structures among them.
  arXiv  Detail & Related papers  (2021-07-02T05:44:56Z)
• Structural Causal Models Are (Solvable by) Credal Networks [70.45873402967297]
  Causal inferences can be obtained by standard algorithms for the updating of credal nets. This contribution should be regarded as a systematic approach to represent structural causal models by credal networks. Experiments show that approximate algorithms for credal networks can immediately be used to do causal inference in real-size problems.
  arXiv  Detail & Related papers  (2020-08-02T11:19:36Z)
• Bayesian Neural Networks [0.0]
  We show how errors in prediction by neural networks can be obtained in principle, and provide the two favoured methods for characterising these errors. We will also describe how both of these methods have substantial pitfalls when put into practice.
  arXiv  Detail & Related papers  (2020-06-02T09:43:00Z)

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.