Kiran Tomlinson, Johan Ugander, Austin R. Benson
arXiv 17 May 2021 · Machine Learning · 3 citations (OpenAlex)
arXiv:2105.07959 · PDF · DOI · OpenAlex · Extracted main text
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.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Arjun Seshadri, Alex Peysakhovich, and Johan Ugander (2019) Discovering Context Effects from Raw Choice Data. In ICML | 1.000 | 9 | 4 | 100% |
| 2 | Frank S Koppelman and Chandra Bhat (2006) A self instructing course in mode choice modeling: multinomial and nested logit models | 1.000 | 7 | 3 | 100% |
| 3 | Kiran Tomlinson and Austin R Benson (2021) Learning Interpretable Feature Context Effects in Discrete Choice. In KDD self | 1.000 | 6 | 3 | 100% |
| 4 | Stephen Ragain and Johan Ugander (2016) Pairwise choice Markov chains. In NeurIPS | 0.928 | 4 | 4 | 100% |
| 5 | Austin R Benson, Ravi Kumar, and Andrew Tomkins (2016) On the relevance of irrelevant alternatives. In WWW self | 0.928 | 4 | 3 | 100% |
| 6 | Amanda Bower and Laura Balzano (2020) Preference Modeling with Context-Dependent Salient Features. In ICML | 0.928 | 4 | 3 | 100% |
| 7 | Guido W Imbens (2004) Nonparametric estimation of average treatment effects under exogeneity: A review | 0.928 | 4 | 3 | 100% |
| 8 | Nir Rosenfeld, Kojin Oshiba, and Yaron Singer (2020) Predicting Choice with Set-Dependent Aggregation. In ICML | 0.928 | 4 | 3 | 100% |
| 9 | Kenneth E Train (2009) Discrete choice methods with simulation | 0.843 | 4 | 3 | 75% |
| 10 | Inderjit S Dhillon (2001) Co-clustering documents and words using bipartite spectral graph partitioning. In KDD | 0.737 | 3 | 2 | 100% |
Showing the top 10 of 57 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Graph-based Methods for Discrete Choice | 0.737 | 3 | 2 |