Allocation Requires Prediction Only if Inequality Is Low

• URL: http://arxiv.org/abs/2406.13882v1
• Date: Wed, 19 Jun 2024 23:23:32 GMT
• Title: Allocation Requires Prediction Only if Inequality Is Low
• Authors: Ali Shirali, Rediet Abebe, Moritz Hardt,
• Abstract summary: We evaluate the efficacy of prediction-based allocations in settings where individuals belong to larger units. We find that prediction-based allocations outperform baseline methods only when between-unit inequality is low and the intervention budget is high.
• Score: 24.57131078538418
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Algorithmic predictions are emerging as a promising solution concept for efficiently allocating societal resources. Fueling their use is an underlying assumption that such systems are necessary to identify individuals for interventions. We propose a principled framework for assessing this assumption: Using a simple mathematical model, we evaluate the efficacy of prediction-based allocations in settings where individuals belong to larger units such as hospitals, neighborhoods, or schools. We find that prediction-based allocations outperform baseline methods using aggregate unit-level statistics only when between-unit inequality is low and the intervention budget is high. Our results hold for a wide range of settings for the price of prediction, treatment effect heterogeneity, and unit-level statistics' learnability. Combined, we highlight the potential limits to improving the efficacy of interventions through prediction.

Related papers

• Weighted Aggregation of Conformity Scores for Classification [9.559062601251464]
  Conformal prediction is a powerful framework for constructing prediction sets with valid coverage guarantees. We propose a novel approach that combines multiple score functions to improve the performance of conformal predictors.
  arXiv  Detail & Related papers  (2024-07-14T14:58:03Z)
• Stratified Prediction-Powered Inference for Hybrid Language Model Evaluation [62.2436697657307]
  Prediction-powered inference (PPI) is a method that improves statistical estimates based on limited human-labeled data. We propose a method called Stratified Prediction-Powered Inference (StratPPI) We show that the basic PPI estimates can be considerably improved by employing simple data stratification strategies.
  arXiv  Detail & Related papers  (2024-06-06T17:37:39Z)
• 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)
• Targeted Machine Learning for Average Causal Effect Estimation Using the Front-Door Functional [3.0232957374216953]
  evaluating the average causal effect (ACE) of a treatment on an outcome often involves overcoming the challenges posed by confounding factors in observational studies. Here, we introduce novel estimation strategies for the front-door criterion based on the targeted minimum loss-based estimation theory. We demonstrate the applicability of these estimators to analyze the effect of early stage academic performance on future yearly income.
  arXiv  Detail & Related papers  (2023-12-15T22:04:53Z)
• The Relative Value of Prediction in Algorithmic Decision Making [0.0]
  We ask: What is the relative value of prediction in algorithmic decision making? We identify simple, sharp conditions determining the relative value of prediction vis-a-vis expanding access. We illustrate how these theoretical insights may be used to guide the design of algorithmic decision making systems in practice.
  arXiv  Detail & Related papers  (2023-12-13T20:52:45Z)
• Distribution-Free Statistical Dispersion Control for Societal Applications [16.43522470711466]
  Explicit finite-sample statistical guarantees on model performance are an important ingredient in responsible machine learning. Previous work has focused mainly on bounding either the expected loss of a predictor or the probability that an individual prediction will incur a loss value in a specified range. We propose a simple yet flexible framework that allows us to handle a much richer class of statistical functionals beyond previous work.
  arXiv  Detail & Related papers  (2023-09-25T00:31:55Z)
• Prediction-Powered Inference [68.97619568620709]
  Prediction-powered inference is a framework for performing valid statistical inference when an experimental dataset is supplemented with predictions from a machine-learning system. The framework yields simple algorithms for computing provably valid confidence intervals for quantities such as means, quantiles, and linear and logistic regression coefficients. Prediction-powered inference could enable researchers to draw valid and more data-efficient conclusions using machine learning.
  arXiv  Detail & Related papers  (2023-01-23T18:59:28Z)
• Efficient and Differentiable Conformal Prediction with General Function Classes [96.74055810115456]
  We propose a generalization of conformal prediction to multiple learnable parameters. We show that it achieves approximate valid population coverage and near-optimal efficiency within class. Experiments show that our algorithm is able to learn valid prediction sets and improve the efficiency significantly.
  arXiv  Detail & Related papers  (2022-02-22T18:37:23Z)
• Double Robust Representation Learning for Counterfactual Prediction [68.78210173955001]
  We propose a novel scalable method to learn double-robust representations for counterfactual predictions. We make robust and efficient counterfactual predictions for both individual and average treatment effects. The algorithm shows competitive performance with the state-of-the-art on real world and synthetic data.
  arXiv  Detail & Related papers  (2020-10-15T16:39:26Z)
• 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)
• Fairness Measures for Regression via Probabilistic Classification [0.0]
  Algorithmic fairness involves expressing notions such as equity, or reasonable treatment, as quantifiable measures that a machine learning algorithm can optimise. This is in part because classification fairness measures are easily computed by comparing the rates of outcomes, leading to behaviours such as ensuring the same fraction of eligible men are selected as eligible women. But such measures are computationally difficult to generalise to the continuous regression setting for problems such as pricing, or allocating payments. For the regression setting we introduce tractable approximations of the independence, separation and sufficiency criteria by observing that they factorise as ratios of different conditional probabilities of the protected attributes.
  arXiv  Detail & Related papers  (2020-01-16T21:53:26Z)

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.