Choice Set Confounding in Discrete Choice

TL;DR

This paper identifies how choice set confounding biases preference learning in discrete choice models and proposes causal inference-based methods to correct for this bias, improving preference estimation and choice prediction.

Contribution

It introduces novel methods adapting causal inference techniques to address choice set confounding in discrete choice models, enhancing preference estimation accuracy.

Findings

Accounting for choice set confounding improves preference estimates.

Methods effectively recover preferences in real-world datasets.

Correcting confounding aligns choices with rational utility-maximization.

Abstract

Standard methods in preference learning involve estimating the parameters of discrete choice models from data of selections (choices) made by individuals from a discrete set of alternatives (the choice set). While there are many models for individual preferences, existing learning methods overlook how choice set assignment affects the data. Often, the choice set itself is influenced by an individual's preferences; for instance, a consumer choosing a product from an online retailer is often presented with options from a recommender system that depend on information about the consumer's preferences. Ignoring these assignment mechanisms can mislead choice models into making biased estimates of preferences, a phenomenon that we call choice set confounding; we demonstrate the presence of such confounding in widely-used choice datasets. To address this issue, we adapt methods from causal inference to the discrete choice setting. We use covariates of the chooser for inverse probability weighting and/or regression controls, accurately recovering individual preferences in the presence of choice set confounding under certain assumptions. When such covariates are unavailable or inadequate, we develop methods that take advantage of structured choice set assignment to improve prediction. We demonstrate the effectiveness of our methods on real-world choice data, showing, for example, that accounting for choice set confounding makes choices observed in hotel booking and commute transportation more consistent with rational utility-maximization.