Counterfactual (Non-)identifiability of Learned Structural Causal Models

• URL: http://arxiv.org/abs/2301.09031v1
• Date: Sun, 22 Jan 2023 00:58:14 GMT
• Title: Counterfactual (Non-)identifiability of Learned Structural Causal Models
• Authors: Arash Nasr-Esfahany, Emre Kiciman
• Abstract summary: We warn practitioners about non-identifiability of counterfactual inference from observational data. We propose a method for estimating worst-case errors of learned DSCMs' counterfactual predictions.
• Score: 10.102073937554488
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advances in probabilistic generative modeling have motivated learning Structural Causal Models (SCM) from observational datasets using deep conditional generative models, also known as Deep Structural Causal Models (DSCM). If successful, DSCMs can be utilized for causal estimation tasks, e.g., for answering counterfactual queries. In this work, we warn practitioners about non-identifiability of counterfactual inference from observational data, even in the absence of unobserved confounding and assuming known causal structure. We prove counterfactual identifiability of monotonic generation mechanisms with single dimensional exogenous variables. For general generation mechanisms with multi-dimensional exogenous variables, we provide an impossibility result for counterfactual identifiability, motivating the need for parametric assumptions. As a practical approach, we propose a method for estimating worst-case errors of learned DSCMs' counterfactual predictions. The size of this error can be an essential metric for deciding whether or not DSCMs are a viable approach for counterfactual inference in a specific problem setting. In evaluation, our method confirms negligible counterfactual errors for an identifiable SCM from prior work, and also provides informative error bounds on counterfactual errors for a non-identifiable synthetic SCM.

Related papers

• Towards Characterizing Domain Counterfactuals For Invertible Latent Causal Models [15.817239008727789]
  In this work, we analyze a specific type of causal query called domain counterfactuals, which hypothesizes what a sample would have looked like if it had been generated in a different domain. We show that recovering the latent Structural Causal Model (SCM) is unnecessary for estimating domain counterfactuals. We also develop a theoretically grounded practical algorithm that simplifies the modeling process to generative model estimation.
  arXiv  Detail & Related papers  (2023-06-20T04:19:06Z)
• 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)
• Deception by Omission: Using Adversarial Missingness to Poison Causal Structure Learning [12.208616050090027]
  Inference of causal structures from observational data is a key component of causal machine learning. Prior work has demonstrated that adversarial perturbations of completely observed training data may be used to force the learning of inaccurate causal structural models. This work introduces a novel attack methodology wherein the adversary deceptively omits a portion of the true training data to bias the learned causal structures.
  arXiv  Detail & Related papers  (2023-05-31T17:14:20Z)
• 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)
• MissDAG: Causal Discovery in the Presence of Missing Data with Continuous Additive Noise Models [78.72682320019737]
  We develop a general method, which we call MissDAG, to perform causal discovery from data with incomplete observations. MissDAG maximizes the expected likelihood of the visible part of observations under the expectation-maximization framework. We demonstrate the flexibility of MissDAG for incorporating various causal discovery algorithms and its efficacy through extensive simulations and real data experiments.
  arXiv  Detail & Related papers  (2022-05-27T09:59:46Z)
• Learning Hidden Markov Models When the Locations of Missing Observations are Unknown [54.40592050737724]
  We consider the general problem of learning an HMM from data with unknown missing observation locations. We provide reconstruction algorithms that do not require any assumptions about the structure of the underlying chain. We show that under proper specifications one can reconstruct the process dynamics as well as if the missing observations positions were known.
  arXiv  Detail & Related papers  (2022-03-12T22:40:43Z)
• Diffusion Causal Models for Counterfactual Estimation [18.438307666925425]
  We consider the task of counterfactual estimation from observational imaging data given a known causal structure. We propose Diff-SCM, a deep structural causal model that builds on recent advances of generative energy-based models. We find that Diff-SCM produces more realistic and minimal counterfactuals than baselines on MNIST data and can also be applied to ImageNet data.
  arXiv  Detail & Related papers  (2022-02-21T12:23:01Z)
• 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)
• 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)

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.