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Prioritized Ranking Experimental Design Using Recommender Systems in Two-Sided Platforms

Mahyar Habibi, Zahra Khanalizadeh, Negar Ziaeian
📄 Read on arXiv
🎤 Presented at CODE@MIT 2024 (October 2024)

Abstract: Interdependencies between units in online two-sided marketplaces complicate estimating causal effects in experimental settings. We propose a novel experimental design to mitigate the interference bias in estimating the total average treatment effect (TATE) of item-side interventions in online two-sided marketplaces. Our Two-Sided Prioritized Ranking (TSPR) design uses the recommender system as an instrument for experimentation. TSPR strategically prioritizes items based on their treatment status in the listings displayed to users. We designed TSPR to provide users with a coherent platform experience by ensuring access to all items and a consistent realization of their treatment by all users. We evaluate our experimental design through simulations using a search impression dataset from an online travel agency. Our methodology closely estimates the true simulated TATE, while a baseline item-side estimator significantly overestimates TATE.

Extending the Reproductive Horizon: Exploring Beliefs, Preferences, and Decision-Making

Negar Ziaeian, Zahra Khanalizadeh

Abstract: Biological constraints on fertility create significant gender disparities in human capital accumulation and professional advancement. While women’s fecundity begins to decline in their early 30s and this decline accelerates after age 35, men face no comparable constraint. This asymmetry not only influences women’s participation in the labor market but also imposes higher costs for delaying marriage and childbearing, contributing to their underrepresentation in high-powered roles. Assisted Reproductive Technologies (ART), such as In Vitro Fertilization (IVF), offer a potential extension of the reproductive horizon, yet their impact on fertility behavior and gender roles remains underexplored. This paper investigates the effect of ART access on the Total Fertility Rate (TFR) and its broader implications for women’s decision-making. In the first phase of this research, we estimate the causal effect of ART access on fertility outcomes. In the second phase, we conduct a survey to explore beliefs and preferences surrounding ART. The survey focuses on individuals’ perceptions of the likelihood of experiencing infertility at various ages, the success rates of IVF if pursued, and the costs associated with IVF treatment. By examining how these beliefs influence decisions related to family planning and fertility treatments, we aim to uncover the role of expectations and beliefs in shaping fertility behavior and career choices.

Distinguishing Biases from Personal Preferences: An ‘Honest’ Machine Learning Approach

Mahyar Habibi, Zahra Khanalizadeh, Negar Ziaeian

Abstract: This study proposes a new method for estimating biases at the micro-level in scenarios with multiple bilateral interactions, where the presence of individual preferences and correlated characteristics complicates the analysis. The proposed method comprises two stages. In the first stage, the method introduces a novel approach to extract preferences and characteristics, employing Collaborative Filtering with an ‘honest’ design. This technique is designed to separate preferences and self-induced outcomes from the constructed embeddings of interacting units. In the second stage, the method utilizes a Double Machine Learning estimator to identify biases at the unit level, based on the embeddings generated in the first stage. The methodology was applied to a dataset of nearly 150,000 film ratings by professional critics, aiming to uncover personal biases among critics towards films directed by women. The results indicate that approximately 5% of critics show a significant bias in favor of films directed by women, once personal preferences and film characteristics are accounted for. However, a ‘naive’ approach that ignores these elements suggests a much higher prevalence of bias among critics.