Inequality Sensitive Optimal Treatment Assignment

Eduardo Zambrano

Home/Archive/arXiv

arXiv · 2409.14776

Inequality Sensitive Optimal Treatment Assignment

Eduardo Zambrano

View original source ↗Read source document ↗Export metadata ↓

Abstract

The egalitarian equivalent, $ee$, of a societal distribution of outcomes with mean $m$ is the outcome level such that the evaluator is indifferent between the distribution of outcomes and a society in which everyone obtains an outcome of $ee$. For an inequality averse evaluator, $ee < m$. In this paper, I extend the optimal treatment choice framework in Manski (2024) to the case where the welfare evaluation is made using egalitarian equivalent measures, and derive optimal treatment rules for the Bayesian, maximin and minimax regret inequality averse evaluators. I illustrate how the methodology operates in the context of the JobCorps education and training program for disadvantaged youth (Schochet, Burghardt, and McConnell 2008) and in Meager (2022)'s Bayesian meta analysis of the microcredit literature.

Explore related subjects

econ.EM

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Eduardo Zambrano. 2025-02-21. Inequality Sensitive Optimal Treatment Assignment. https://arxiv.org/abs/2409.14776

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Summary Indices in Treatment Effect Estimation

This paper studies the practice of combining multiple outcomes into a summary index to estimate a causal effect. For common estimators and index constructions, the estimate equals a weighted sum of the estimated effects on the components, with weights that are implicit and rarely reported. The paper derives the weights and shows that, for inverse-covariance-weighted indices, they can be negative and unrestricted in magnitude, so the index effect can have the opposite sign to every component effect. The paper proposes two procedures for valid inference on the index effect: a variance estimator that accounts for the data-dependent weights, and a shifted t-test that requires no such correction. Conventional t-tests of the null of no effect remain valid. Contrary to common claims, summary indices do not generally improve power. Three published studies illustrate the results.

econ.EM↗

arXiv2026

The "Rough" HAR Model

This paper proposes discrete-time approximations to rough continuous-time models of realized variance (RV). The leading rough models can be viewed as autoregressive processes driven by fractional Gaussian noise. We show that the Wold representation of this noise concentrates its dependence at the first lag when the Hurst parameter is below one half. Augmenting the autoregressive (AR) and heterogeneous autoregressive (HAR) models with a first-order moving-average (MA(1)) component therefore approximates the roughness, and the MA coefficient maps almost linearly into the Hurst parameter. We refer to these extensions as the "rough" AR and "rough" HAR models. Estimating them on the log RV of ten ETFs, we find negative MA coefficients for every asset, and the implied Hurst parameters align closely with the estimates from the continuous-time models. In the HAR literature, the negative MA(1) component is a significant feature that has been largely overlooked. In out-of-sample comparisons, the "rough" models outperform their classical counterparts for nearly every asset and horizon, with the largest gains at short horizons, and their accuracy is comparable to that of the rough continuous-time models but much easier to estimate by standard off-the-shelf software.

econ.EM↗

arXiv2026

Match forecasts in UEFA club competitions: Elo ratings versus Transfermarkt valuations

The pre-season strengths of European football clubs are usually measured by two proxies in the literature. Football Club Elo Ratings provide strictly performance-based Elo ratings from the early days of the European Cups, while Transfermarkt valuations are crowd-based estimates of squad market values. This paper compares them by evaluating their ability to forecast the results of matches played in the UEFA Champions League and the UEFA Europa League between the seasons 2020/21 and 2024/25. The two indicators yield almost identical out-of-sample accuracy when used separately. Combining the two measures leads to a modest improvement, but the best aggregation procedure is sensitive to the forecast target. Our results suggest that seeding based on Elo ratings would be (closely) optimal.

econ.EM↗