Causal Post-Processing of Predictive Models

• URL: http://arxiv.org/abs/2406.09567v2
• Date: Mon, 12 May 2025 05:54:27 GMT
• Title: Causal Post-Processing of Predictive Models
• Authors: Carlos Fernández-Loría, Yanfang Hou, Foster Provost, Jennifer Hill,
• Abstract summary: We propose causal post-processing, a family of techniques for refining predictive scores to better align with causal effects.<n>We introduce three main CPP approaches -- monotonic post-processing, correction post-processing, and model-based post-processing.<n>Our findings highlight the advantages of integrating non-causal predictive models with experimental data, rather than treating them as competing alternatives.
• Score: 1.3124513975412255
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Decision makers across various domains rely on predictive models to guide individual-level intervention decisions. However, these models are typically trained to predict outcomes rather than causal effects, leading to misalignments when they are used for causal decision making. Experimental data to train effective causal effect models often is limited. To address this issue, we propose causal post-processing (CPP), a family of techniques for refining predictive scores to better align with causal effects using limited experimental data. Rather than training separate causal models for each intervention, causal post-processing can adapt existing predictive scores to support different decision-making requirements, such as estimating effect sizes, ranking individuals by expected effects, or classifying individuals based on an intervention threshold. We introduce three main CPP approaches -- monotonic post-processing, correction post-processing, and model-based post-processing -- each balancing statistical efficiency and flexibility differently. Through simulations and an empirical application in advertising, we demonstrate that causal post-processing improves intervention decisions, particularly in settings where experimental data is expensive or difficult to obtain at scale. Our findings highlight the advantages of integrating non-causal predictive models with experimental data, rather than treating them as competing alternatives, which provides a scalable and data-efficient approach to causal inference for decision making.

Related papers

• The Amenability Framework: Rethinking Causal Ordering Without Estimating Causal Effects [1.6114012813668932]
  We propose a conceptual framework based on an individual's latent proclivity to be influenced by an intervention. We then formalize conditions under which predictive scores serve as effective proxies for amenability. We show that, under plausible assumptions, predictive models can outperform causal effect estimators in ranking individuals by intervention effects.
  arXiv  Detail & Related papers  (2025-04-03T10:20:48Z)
• Black Box Causal Inference: Effect Estimation via Meta Prediction [56.277798874118425]
  We frame causal inference as a dataset-level prediction problem, offloading algorithm design to the learning process.<n>We introduce, called black box causal inference (BBCI), builds estimators in a black-box manner by learning to predict causal effects from sampled dataset-effect pairs.<n>We demonstrate accurate estimation of average treatment effects (ATEs) and conditional average treatment effects (CATEs) with BBCI across several causal inference problems.
  arXiv  Detail & Related papers  (2025-03-07T23:43:19Z)
• Generative Intervention Models for Causal Perturbation Modeling [80.72074987374141]
  In many applications, it is a priori unknown which mechanisms of a system are modified by an external perturbation. We propose a generative intervention model (GIM) that learns to map these perturbation features to distributions over atomic interventions.
  arXiv  Detail & Related papers  (2024-11-21T10:37:57Z)
• Influence Functions for Scalable Data Attribution in Diffusion Models [52.92223039302037]
  Diffusion models have led to significant advancements in generative modelling. Yet their widespread adoption poses challenges regarding data attribution and interpretability. We develop an influence functions framework to address these challenges.
  arXiv  Detail & Related papers  (2024-10-17T17:59:02Z)
• Conformal Diffusion Models for Individual Treatment Effect Estimation and Inference [6.406853903837333]
  Individual treatment effect offers the most granular measure of treatment effect on an individual level. We propose a novel conformal diffusion model-based approach that addresses those intricate challenges.
  arXiv  Detail & Related papers  (2024-08-02T21:35:08Z)
• C-XGBoost: A tree boosting model for causal effect estimation [8.246161706153805]
  Causal effect estimation aims at estimating the Average Treatment Effect as well as the Conditional Average Treatment Effect of a treatment to an outcome from the available data. We propose a new causal inference model, named C-XGBoost, for the prediction of potential outcomes.
  arXiv  Detail & Related papers  (2024-03-31T17:43:37Z)
• 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 prediction and behavioural analysis of machine learning methods for modelling travel mode choice [0.26249027950824505]
  We conduct a systematic comparison of different modelling approaches, across multiple modelling problems, in terms of the key factors likely to affect model choice. Results indicate that the models with the highest disaggregate predictive performance provide poorer estimates of behavioural indicators and aggregate mode shares. It is also observed that the MNL model performs robustly in a variety of situations, though ML techniques can improve the estimates of behavioural indices such as Willingness to Pay.
  arXiv  Detail & Related papers  (2023-01-11T11:10:32Z)
• Out-of-sample scoring and automatic selection of causal estimators [0.0]
  We propose novel scoring approaches for both the CATE case and an important subset of instrumental variable problems. We implement that in an open source package that relies on DoWhy and EconML libraries.
  arXiv  Detail & Related papers  (2022-12-20T08:29:18Z)
• An evaluation framework for comparing causal inference models [3.1372269816123994]
  We use the proposed evaluation methodology to compare several state-of-the-art causal effect estimation models. The main motivation behind this approach is the elimination of the influence of a small number of instances or simulation on the benchmarking process.
  arXiv  Detail & Related papers  (2022-08-31T21:04:20Z)
• Measuring Causal Effects of Data Statistics on Language Model's `Factual' Predictions [59.284907093349425]
  Large amounts of training data are one of the major reasons for the high performance of state-of-the-art NLP models. We provide a language for describing how training data influences predictions, through a causal framework. Our framework bypasses the need to retrain expensive models and allows us to estimate causal effects based on observational data alone.
  arXiv  Detail & Related papers  (2022-07-28T17:36:24Z)
• Inferring Effect Ordering Without Causal Effect Estimation [1.6114012813668932]
  Predictive models are often employed to guide interventions across various domains, such as advertising, customer retention, and personalized medicine. Our paper addresses the question of when and how these predictive models can be interpreted causally. We formalize two assumptions, full latent mediation and latent monotonicity, that are jointly sufficient for inferring effect ordering without direct causal effect estimation.
  arXiv  Detail & Related papers  (2022-06-25T02:15:22Z)
• Active Bayesian Causal Inference [72.70593653185078]
  We propose Active Bayesian Causal Inference (ABCI), a fully-Bayesian active learning framework for integrated causal discovery and reasoning. ABCI jointly infers a posterior over causal models and queries of interest. We show that our approach is more data-efficient than several baselines that only focus on learning the full causal graph.
  arXiv  Detail & Related papers  (2022-06-04T22:38:57Z)
• Generalizability of Machine Learning Models: Quantitative Evaluation of Three Methodological Pitfalls [1.3870303451896246]
  We implement random forest and deep convolutional neural network models using several medical imaging datasets. We show that violation of the independence assumption could substantially affect model generalizability. Inappropriate performance indicators could lead to erroneous conclusions.
  arXiv  Detail & Related papers  (2022-02-01T05:07:27Z)
• 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)
• 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)
• Selecting Treatment Effects Models for Domain Adaptation Using Causal Knowledge [82.5462771088607]
  We propose a novel model selection metric specifically designed for ITE methods under the unsupervised domain adaptation setting. In particular, we propose selecting models whose predictions of interventions' effects satisfy known causal structures in the target domain.
  arXiv  Detail & Related papers  (2021-02-11T21:03:14Z)
• Counterfactual Predictions under Runtime Confounding [74.90756694584839]
  We study the counterfactual prediction task in the setting where all relevant factors are captured in the historical data. We propose a doubly-robust procedure for learning counterfactual prediction models in this setting.
  arXiv  Detail & Related papers  (2020-06-30T15:49:05Z)

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.