Inferring Effect Ordering Without Causal Effect Estimation

• URL: http://arxiv.org/abs/2206.12532v6
• Date: Thu, 27 Feb 2025 01:42:13 GMT
• Title: Inferring Effect Ordering Without Causal Effect Estimation
• Authors: Carlos Fernández-Loría, Jorge Loría,
• Abstract summary: We investigate when and why predictions can reliably rank individuals by causal effects in settings where direct effect estimation is infeasible.<n>We introduce three key conditions -- full latent moderation, full latent mediation, and latent monotonicity -- that determine when scores can recover causal-effect rankings and, in some cases, even outperform direct effect estimation.
• Score: 1.6114012813668932
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Predictive models that estimate outcome probabilities are widely used to guide interventions in applications such as advertising, customer retention, and behavioral nudging. Although these outcome probabilities do not measure causal effects, they are often treated as proxies for identifying individuals with the highest intervention impact. We investigate when and why these predictions (which we refer to as scores) can reliably rank individuals by their causal effects in settings where direct effect estimation is infeasible. The key mechanism underlying this approach is that scores serve as proxies for a latent moderator that drives variation in causal effects. Building on this foundation, we introduce three key conditions -- full latent moderation, full latent mediation, and latent monotonicity -- that determine when scores can recover causal-effect rankings and, in some cases, even outperform direct effect estimation. To support practical applications, we provide guidelines for assessing when scores are viable proxies, particularly in contexts lacking data on new interventions or with delayed outcome measurements. Our findings demonstrate that effect heterogeneity can be leveraged through predictive modeling when the target variable being modeled captures a strong latent moderator, expanding the scope of causal inference beyond traditional effect estimation and, in some cases, reducing the need for large-scale randomized experiments.

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)
• Microfoundation Inference for Strategic Prediction [26.277259491014163]
  We propose a methodology for learning the distribution map that encapsulates the long-term impacts of predictive models on the population. Specifically, we model agents' responses as a cost-utility problem and propose estimates for said cost. We provide a rate of convergence for this proposed estimate and assess its quality through empirical demonstrations on a credit-scoring dataset.
  arXiv  Detail & Related papers  (2024-11-13T19:37:49Z)
• Performative Prediction on Games and Mechanism Design [69.7933059664256]
  We study a collective risk dilemma where agents decide whether to trust predictions based on past accuracy. As predictions shape collective outcomes, social welfare arises naturally as a metric of concern. We show how to achieve better trade-offs and use them for mechanism design.
  arXiv  Detail & Related papers  (2024-08-09T16:03:44Z)
• Causal Fine-Tuning and Effect Calibration of Non-Causal Predictive Models [1.3124513975412255]
  This paper proposes techniques to enhance the performance of non-causal models for causal inference using data from randomized experiments. In domains like advertising, customer retention, and precision medicine, non-causal models that predict outcomes under no intervention are often used to score individuals and rank them according to the expected effectiveness of an intervention.
  arXiv  Detail & Related papers  (2024-06-13T20:18:16Z)
• Automating the Selection of Proxy Variables of Unmeasured Confounders [16.773841751009748]
  We extend the existing proxy variable estimator to accommodate scenarios where multiple unmeasured confounders exist between the treatments and the outcome. We propose two data-driven methods for the selection of proxy variables and for the unbiased estimation of causal effects.
  arXiv  Detail & Related papers  (2024-05-25T08:53:49Z)
• Reduced-Rank Multi-objective Policy Learning and Optimization [57.978477569678844]
  In practice, causal researchers do not have a single outcome in mind a priori. In government-assisted social benefit programs, policymakers collect many outcomes to understand the multidimensional nature of poverty. We present a data-driven dimensionality-reduction methodology for multiple outcomes in the context of optimal policy learning.
  arXiv  Detail & Related papers  (2024-04-29T08:16:30Z)
• Decoding Susceptibility: Modeling Misbelief to Misinformation Through a Computational Approach [61.04606493712002]
  Susceptibility to misinformation describes the degree of belief in unverifiable claims that is not observable. Existing susceptibility studies heavily rely on self-reported beliefs. We propose a computational approach to model users' latent susceptibility levels.
  arXiv  Detail & Related papers  (2023-11-16T07:22:56Z)
• On the Actionability of Outcome Prediction [8.32379926107182]
  Practitioners recognize that the ultimate goal is not just to predict but to act effectively. We ask: when are accurate predictors of outcomes helpful for identifying the most suitable intervention? We find that except in cases where there is a single decisive action for improving the outcome, outcome prediction never maximizes "action value"
  arXiv  Detail & Related papers  (2023-09-08T17:57:31Z)
• Doubly Robust Estimation of Direct and Indirect Quantile Treatment Effects with Machine Learning [0.0]
  We suggest a machine learning estimator of direct and indirect quantile treatment effects under a selection-on-observables assumption. The proposed method is based on the efficient score functions of the cumulative distribution functions of potential outcomes. We also propose a multiplier bootstrap for statistical inference and show the validity of the multiplier.
  arXiv  Detail & Related papers  (2023-07-03T14:27:15Z)
• 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)
• Disentangled Representation for Causal Mediation Analysis [25.114619307838602]
  Causal mediation analysis is a method that is often used to reveal direct and indirect effects. Deep learning shows promise in mediation analysis, but the current methods only assume latent confounders that affect treatment, mediator and outcome simultaneously. We propose the Disentangled Mediation Analysis Variational AutoEncoder (DMAVAE), which disentangles the representations of latent confounders into three types to accurately estimate the natural direct effect, natural indirect effect and total effect.
  arXiv  Detail & Related papers  (2023-02-19T23:37:17Z)
• Zero-shot causal learning [64.9368337542558]
  CaML is a causal meta-learning framework which formulates the personalized prediction of each intervention's effect as a task. We show that CaML is able to predict the personalized effects of novel interventions that do not exist at the time of training.
  arXiv  Detail & Related papers  (2023-01-28T20:14:11Z)
• Neighborhood Adaptive Estimators for Causal Inference under Network Interference [152.4519491244279]
  We consider the violation of the classical no-interference assumption, meaning that the treatment of one individuals might affect the outcomes of another. To make interference tractable, we consider a known network that describes how interference may travel. We study estimators for the average direct treatment effect on the treated in such a setting.
  arXiv  Detail & Related papers  (2022-12-07T14:53:47Z)
• Empirical Estimates on Hand Manipulation are Recoverable: A Step Towards Individualized and Explainable Robotic Support in Everyday Activities [80.37857025201036]
  Key challenge for robotic systems is to figure out the behavior of another agent. Processing correct inferences is especially challenging when (confounding) factors are not controlled experimentally. We propose equipping robots with the necessary tools to conduct observational studies on people.
  arXiv  Detail & Related papers  (2022-01-27T22:15:56Z)
• Deconfounding Scores: Feature Representations for Causal Effect Estimation with Weak Overlap [140.98628848491146]
  We introduce deconfounding scores, which induce better overlap without biasing the target of estimation. We show that deconfounding scores satisfy a zero-covariance condition that is identifiable in observed data. In particular, we show that this technique could be an attractive alternative to standard regularizations.
  arXiv  Detail & Related papers  (2021-04-12T18:50:11Z)
• 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)
• Nonparametric inference for interventional effects with multiple mediators [0.0]
  We provide theory that allows for more flexible, possibly machine learning-based, estimation techniques. We demonstrate multiple robustness properties of the proposed estimators. Our work thus provides a means of leveraging modern statistical learning techniques in estimation of interventional mediation effects.
  arXiv  Detail & Related papers  (2020-01-16T19:05:00Z)

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.