# Shape-constrained partial identification of a population mean under unknown probabilities of sample selection

## TL;DR

This paper introduces a method to improve bounds on population mean estimates under unknown sample selection probabilities by incorporating shape constraints like symmetry or log-concavity, demonstrated through an educational test case.

## Contribution

It develops a novel approach that leverages auxiliary shape constraints to tighten bounds on population means with unknown sampling probabilities.

## Key findings

- Tighter bounds achieved using shape constraints.
- Application to estimating Aymara students' test performance.
- Implementation in R package 'scbounds'.

## Abstract

A prevailing challenge in the biomedical and social sciences is to estimate a population mean from a sample obtained with unknown selection probabilities. Using a well-known ratio estimator, Aronow and Lee (2013) proposed a method for partial identification of the mean by allowing the unknown selection probabilities to vary arbitrarily between two fixed extreme values. In this paper, we show how to leverage auxiliary shape constraints on the population outcome distribution, such as symmetry or log-concavity, to obtain tighter bounds on the population mean. We use this method to estimate the performance of Aymara students---an ethnic minority in the north of Chile---in a national educational standardized test. We implement this method in the new statistical software package scbounds for R.