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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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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
Download tables as Excel
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

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