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dsl: Design-based Supervised Learning

Overview

R package dsl implements design-based supervised learning (DSL) proposed in Egami, Hinck, Stewart, and Wei (2023).

DSL is a general estimation framework for using predicted variables in statistical analyses. The package is especially useful for researchers trying to use large language models (LLMs) to annotate a large number of documents they analyze subsequently. DSL allows users to obtain statistically valid estimates and standard errors, even when LLM annotations contain arbitrary non-random prediction errors and biases.

Note: While Egami, Hinck, Stewart, and Wei (2023) focused on settings where the outcome variable is text-based and requires prediction, this package allows users to incorporate more general cases where any subset of the outcome and independent variables are text-based, relying on new results derived in our forthcoming paper.

To learn how to use the package, please start with Get Started Page.

Installation Instructions

You can install the most recent development version using the devtools package. First you have to install devtools using the following code. Note that you only have to do this once:

Then, load devtools and use the function install_github() to install dsl:

library(devtools)
install_github("naoki-egami/dsl", dependencies = TRUE)

Information

Authors:

Reference:

  • Egami, Hinck, Stewart, and Wei. (2023). Using Imperfect Surrogates for Downstream Inference: Design-based Supervised Learning for Social Science Applications of Large Language Models, Advances in Neural Information Processing Systems (NeurIPS).