Mechanism Learning: reverse causal inference in the presence of multiple unknown confounding through causally weighted Gaussian mixture models

• URL: http://arxiv.org/abs/2410.20057v2
• Date: Sun, 02 Nov 2025 01:32:23 GMT
• Title: Mechanism Learning: reverse causal inference in the presence of multiple unknown confounding through causally weighted Gaussian mixture models
• Authors: Jianqiao Mao, Max A. Little,
• Abstract summary: A major limitation of machine learning (ML) prediction models is that they recover associational, rather than causal, predictive relationships between variables.<n>This paper proposes mechanism learning, a simple method which uses causally weighted Gaussian Mixture Models (CW-GMMs) to deconfound observational data.<n>We test our method on fully synthetic, semi-synthetic and real-world datasets, demonstrating that it can discover reliable, unbiased, causal ML predictors.
• Score: 0.880899367147235
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: A major limitation of machine learning (ML) prediction models is that they recover associational, rather than causal, predictive relationships between variables. In high-stakes automation applications of ML this is problematic, as the model often learns spurious, non-causal associations. This paper proposes mechanism learning, a simple method which uses causally weighted Gaussian Mixture Models (CW-GMMs) to deconfound observational data such that any appropriate ML model is forced to learn predictive relationships between effects and their causes (reverse causal inference), despite the potential presence of multiple unknown and unmeasured confounding. Effect variables can be very high-dimensional, and the predictive relationship nonlinear, as is common in ML applications. This novel method is widely applicable, the only requirement is the existence of a set of mechanism variables mediating the cause (prediction target) and effect (feature data), which is independent of the (unmeasured) confounding variables. We test our method on fully synthetic, semi-synthetic and real-world datasets, demonstrating that it can discover reliable, unbiased, causal ML predictors where by contrast, the same ML predictor trained naively using classical supervised learning on the original observational data, is heavily biased by spurious associations. We provide code to implement the results in the paper, online.

Related papers

• Debiasing Reward Models by Representation Learning with Guarantees [20.170532007501883]
  We propose a principled framework that mitigates spurious correlation issues in reward models.<n>We show that non-spurious latent variables can be theoretically identified from data, regardless of whether a surrogate for the spurious latent variables is available.<n>Experiments on synthetic and real-world datasets demonstrate that our method effectively mitigates spurious correlation issues and yields more robust reward models.
  arXiv  Detail & Related papers  (2025-10-27T18:37:57Z)
• Semi-Supervised Learning under General Causal Models [51.90307793476367]
  Semi-supervised learning (SSL) aims to train a machine learning model using both labelled and unlabelled data.<n>We propose a framework that works with general causal models in which the variables have flexible causal relations.<n>The learned causal generative model can generate synthetic labelled data for training a more accurate predictive model.
  arXiv  Detail & Related papers  (2025-10-26T07:46:38Z)
• Approaching Maximal Information Extraction in Low-Signal Regimes via Multiple Instance Learning [0.0]
  We propose a new machine learning (ML) methodology to obtain more precise predictions.<n>We show that it might be possible to extract the theoretical maximum Fisher Information latent in a dataset.
  arXiv  Detail & Related papers  (2025-08-09T22:46:55Z)
• Efficient Machine Unlearning via Influence Approximation [75.31015485113993]
  Influence-based unlearning has emerged as a prominent approach to estimate the impact of individual training samples on model parameters without retraining.<n>This paper establishes a theoretical link between memorizing (incremental learning) and forgetting (unlearning)<n>We introduce the Influence Approximation Unlearning algorithm for efficient machine unlearning from the incremental perspective.
  arXiv  Detail & Related papers  (2025-07-31T05:34:27Z)
• Internal Causal Mechanisms Robustly Predict Language Model Out-of-Distribution Behaviors [61.92704516732144]
  We show that the most robust features for correctness prediction are those that play a distinctive causal role in the model's behavior.<n>We propose two methods that leverage causal mechanisms to predict the correctness of model outputs.
  arXiv  Detail & Related papers  (2025-05-17T00:31:39Z)
• The Multiple Dimensions of Spuriousness in Machine Learning [3.475875199871536]
  Learning correlations from data forms the foundation of today's machine learning (ML) and artificial intelligence (AI) research. While such an approach enables the automatic discovery of patterned relationships within big data corpora, it is susceptible to failure modes when unintended correlations are captured. This vulnerability has expanded interest in interrogating spuriousness, often critiqued as an impediment to model performance, fairness, and robustness.
  arXiv  Detail & Related papers  (2024-11-07T13:29:32Z)
• Double Machine Learning meets Panel Data -- Promises, Pitfalls, and Potential Solutions [0.0]
  Estimating causal effect using machine learning (ML) algorithms can help to relax functional form assumptions if used within appropriate frameworks. We show how we can adapt machine learning (DML) for panel data in the presence of unobserved heterogeneity. We also show that the influence of the unobserved heterogeneity on the observed confounders plays a significant role for the performance of most alternative methods.
  arXiv  Detail & Related papers  (2024-09-02T13:59:54Z)
• 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)
• Assumption-Lean and Data-Adaptive Post-Prediction Inference [1.5050365268347254]
  We introduce PoSt-Prediction Adaptive inference (PSPA) that allows valid and powerful inference based on ML-predicted data. We demonstrate the statistical superiority and broad applicability of our method through simulations and real-data applications.
  arXiv  Detail & Related papers  (2023-11-23T22:41:30Z)
• Can predictive models be used for causal inference? [0.0]
  Supervised machine learning (ML) and deep learning (DL) algorithms excel at predictive tasks. It is commonly assumed that they often do so by exploiting non-causal correlations. We show that this trade-off between explanation and prediction is not as deep and fundamental as expected.
  arXiv  Detail & Related papers  (2023-06-18T13:11:36Z)
• 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)
• ER: Equivariance Regularizer for Knowledge Graph Completion [107.51609402963072]
  We propose a new regularizer, namely, Equivariance Regularizer (ER) ER can enhance the generalization ability of the model by employing the semantic equivariance between the head and tail entities. The experimental results indicate a clear and substantial improvement over the state-of-the-art relation prediction methods.
  arXiv  Detail & Related papers  (2022-06-24T08:18:05Z)
• On the Generalization and Adaption Performance of Causal Models [99.64022680811281]
  Differentiable causal discovery has proposed to factorize the data generating process into a set of modules. We study the generalization and adaption performance of such modular neural causal models. Our analysis shows that the modular neural causal models outperform other models on both zero and few-shot adaptation in low data regimes.
  arXiv  Detail & Related papers  (2022-06-09T17:12:32Z)
• Investigating Compounding Prediction Errors in Learned Dynamics Models [7.237751303770201]
  Accurately predicting the consequences of agents' actions is a key prerequisite for planning in robotic control. Deep MBRL has become a popular candidate, using a neural network to learn a dynamics model that predicts with each pass from high-dimensional states to actions. These "one-step" predictions are known to become inaccurate over longer horizons of composed prediction.
  arXiv  Detail & Related papers  (2022-03-17T22:24:38Z)
• The worst of both worlds: A comparative analysis of errors in learning from data in psychology and machine learning [17.336655978572583]
  Recent concerns that machine learning (ML) may be facing a misdiagnosis and replication crisis suggest that some published claims in ML research cannot be taken at face value. A deeper understanding of what concerns in research in supervised ML have in common with the replication crisis in experimental science can put the new concerns in perspective.
  arXiv  Detail & Related papers  (2022-03-12T18:26:24Z)
• Counterfactual Maximum Likelihood Estimation for Training Deep Networks [83.44219640437657]
  Deep learning models are prone to learning spurious correlations that should not be learned as predictive clues. We propose a causality-based training framework to reduce the spurious correlations caused by observable confounders. We conduct experiments on two real-world tasks: Natural Language Inference (NLI) and Image Captioning.
  arXiv  Detail & Related papers  (2021-06-07T17:47:16Z)
• 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)
• The Predictive Normalized Maximum Likelihood for Over-parameterized Linear Regression with Norm Constraint: Regret and Double Descent [12.929639356256928]
  We show that modern machine learning models do not obey a trade-off between the complexity of a prediction rule and its ability to generalize. We use the recently proposed predictive normalized maximum likelihood (pNML) which is the min-max regret solution for individual data. We demonstrate the use of the pNML regret as a point-wise learnability measure on synthetic data and that it can successfully predict the double-decent phenomenon.
  arXiv  Detail & Related papers  (2021-02-14T15:49:04Z)
• Localized Debiased Machine Learning: Efficient Inference on Quantile Treatment Effects and Beyond [69.83813153444115]
  We consider an efficient estimating equation for the (local) quantile treatment effect ((L)QTE) in causal inference. Debiased machine learning (DML) is a data-splitting approach to estimating high-dimensional nuisances. We propose localized debiased machine learning (LDML), which avoids this burdensome step.
  arXiv  Detail & Related papers  (2019-12-30T14:42:52Z)

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.