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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

Choice Set Confounding in Discrete Choice.

KDD, pp.1571-1581, (2021)

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chooser dependent choicecontext effectassignment mechanisminverse probability weightingconditional logitMore(22+)

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 d...More

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Highlights

Individual choices drive the success of businesses and public policy, so predicting and understanding them has far-reaching applications in, e.g., environmental policy [12], marketing [3], Web search [21], and recommender systems [55]
Choice set confounding is a major issue for recent machine learning methods whose success is due to capturing deviations from the traditional principles of rational utility maximization that underlie the workhorse multinomial logit model [30]. (Unlike older econometric models of “irrational” behavior [50, 54], these recent methods are practical for modern, large-scale datasets.) These deviations are known as context effects, and occur whenever the choice set has an influence on a chooser’s preferences
Beyond emphasizing a need for caution, we establish a duality between models accounting for context effects and models accounting for choice set confounding; we show that a model equivalent to the context-dependent random utility model (CDM)—which was designed with context effects in mind—can be derived purely from the perspective of choice set confounding
Choice set confounding is widespread in choice data and can affect choice probability estimates, alter or introduce context effects, and result in poor generalization to new data
Covariates may be more informative about choice sets than preferences, in some cases making inverse probability weighting (IPW) a more viable option than regression
We would expect CDM to significantly outperform logit and MCDM to significantly outperform multinomial logit (MNL)
Initial research on the San Francisco (SF) transportation data used extensive nested logit modeling to account for independence of irrelevant alternatives (IIA) violations [25], which we can manage with choice set confounding

Tables

Table1: Discrete choice models. The item and chooser feature vectors and are part of the dataset, while ∈ R, ∈ R , ∈ R , and ∈ R × are learned parameters
Table2: Context effect models. ∈ R, ∈ R , ∈ R , ∈ R × , ∈ R × are learned parameters
Table3: Regularity violations in sf-work and sf-shop, impossible under mixed logit. Including additional item(s) appears to increase the probability that DA or DA/SR is chosen. The differences are significant according to Fisher’s exact test (sf-work: = 6.5 × 10−9, sf-shop: = 0.005)
Table4: Likelihood gains in sf-work, sf-shop, and expedia from covariates and context with likelihood ratio test (LRT) -values. Δl denotes improvement in log-likelihood
Table5: Log-likelihoods and estimated random-set loglikelihoods with IPW on expedia. After adjusting for confounding, the data is far easier to explain

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Funding

This research was supported by ARO MURI, ARO Awards W911NF191-0057 and 73348- NS-YIP, NSF Award DMS-1830274, the Koret Foundation, and JP Morgan Chase & Co

Study subjects and analysis

samples: 10000

Higher confounding strength results in sets containing items more preferred by. Each trial consists of 10000 samples. Item embeddings are unobserved, but chooser embeddings are used as covariates

Reference

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Kiran Tomlinson

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Johan Ugander

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Austin R. Benson

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