Ziyao Tang (唐子瑶)

Working Papers

• Data Valuation in Marketing Collaborations, with Guang Zeng and Paul B. Ellickson (Job Market Paper, Revise and Resubmit at Management Science, Wharton AI & Analytics for Business Data Grant)

Abstract: This paper demonstrates that the value of data can depend on incentive structure. We study a co-branded credit card partnership between a retailer and a bank, focusing on approval decisions. By analyzing treatment effect heterogeneity, we find customers profitable to the bank reduce the retailer's profit, and vice versa, revealing incentive misalignment. Using counterfactual analysis, we show that retail data benefits the bank (+0.72 local dollar per applicant), but harms the retailer (-0.88) because it helps the bank identify customers that are aligned with its objectives but not retailer's. When a participation constraint is added to ensure both parties benefit, joint gains are positive but modest (+0.73). In contrast, when examining a partnership using a linear contract structure, the value of data is over 40 times greater (+32.77). These findings demonstrate that data's value is not intrinsic but shaped by how decisions are made and how gains are allocated between partners.

• Transparency and Hostility: The Unintended Effects of Geographic Disclosure on Online Identity Attacks, with Guang Zeng and Huaxia Rui (Draft coming soon)

Abstract: This paper examines the causal effect of mandatory location disclosure on identity attacks in online discussions. We analyze a natural experiment on Zhihu, China's largest question-and-answer platform, which implemented a policy in May 2022 requiring the display of users' geographic locations. Using causal inference methods, we find that location disclosure significantly increases the likelihood of identity attacks in user comments. While the policy was designed to enhance accountability through transparency, our results demonstrate that displaying location information  amplifies regional hostility. The policy's impact varies systematically with cultural distance between user pairs. Identity attacks become more frequent when commenters and recipients are from culturally distant regions, with interactions at the greatest cultural distance exhibiting approximately 30% more identity attacks compared to those at the closest distance. These findings reveal unintended consequences of transparency-focused design choices and provide critical insights for platform governance and online discourse moderation.

• When Linear IV Estimator Fails: Avoiding Pitfalls in Causal Effect Estimation in Targeted Marketing, with  Guang Zeng and Paul B. Ellickson

Abstract: Linear estimators may fail to recover causal effects in the presence of treatment effect heterogeneity, introducing bias. While prior literature recommends nonparametric approaches to eliminate this bias, these estimators often suffer from high variance. We show that in targeting applications - where the goal aligns more closely with predictive performance - linear estimators can outperform their nonparametric counterparts by accepting a small amount of bias in exchange for substantial variance reduction. This highlights an important bias-variance tradeoff in causal effect estimation for decision-making contexts.

Work in Progress

• Targeting as Exploration, with Guang Zeng and Paul B. Ellickson

Abstract: Many targeting problems rely on supervised policy learning algorithms. However, in marketing applications, interventions often take time to produce observable outcomes, which limits the ability to update targeting strategies promptly. This paper reframes the targeting problem as a contextual bandit problem. By integrating causal inference techniques with bandit algorithms, we propose a targeting approach that balances exploration and exploitation. Our results demonstrate that incorporating exploration improves efficiency relative to traditional supervised learning methods, particularly in environments with delayed feedback.