Author Archives: btanz

Improving Treatment Effect Estimators Through Experiment Splitting, Coey & Cunningham, Facebook, 2019 TLDR; Experiment splitting can be an effective method to borrow strength across similar experiments and metrics to improve the performance of treatment effect estimators. Using data from 226 Facebook News Feed experiments, the paper shows that using a Lasso estimator based on experimentContinue reading “Improving Treatment Effect Estimators Through Experiment Splitting”

Uplift Modeling for Multiple Treatments with Cost Optimization, Zhao et. al., arXiv, 2019 TLDR; Meta-Learning is an effective method to optimise and personalise delivery of designs, content, products, campaigns or messages for maximal uplift and value. The paper introduces the notion of Net Value Conditional Average Treatment Effect (Net Value CATE), shows how to estimateContinue reading “Uplift Modeling for Multiple Treatments with Cost Optimization”

Position Ranking and Auctions for Online Marketplaces, Chu et. al., Management Science, 2020 The January issue of Management Science has a very impressive paper about optimal position ranking under information asymmetry. It is impressive because (a) it exactly models the problem many marketplaces – especially those selling physical goods – are facing and (b) itContinue reading “Position Ranking and Auctions for Online Marketplaces”

Deep Landscape Forecasting for Real-time Bidding Advertising, Ren et. al., 2019 The topic of this week’s paper is predicting the price distribution in RTB auctions. More specifically, the goal is to predict the price landscape given various parameters of the auction request, such as date and user information. Such a forecast is useful for profitContinue reading “Deep Landscape Forecasting for Real-time Bidding Advertising”

Online Learning for Measuring Incentive Compatibiliy in Ad Auctions, Feng et al., Facebook Research 2019 This week’s paper is about online learning methods to measure the difference in utility between bidding truthfully and following the optimal bidding strategy when the auction mechanism does not satisfy incentive compatibility (IC) and is a black box to bidders.Continue reading “Online Learning for Measuring Incentive Compatibility in Ad Auctions”

Pacing Equilibrium in First-Price Auction Markets, Conitzer et al., arXiv 2019 Our first paper is relevant to Internet advertising as it studies the repeated auctioning of individual items within a larger system managing budgets and budget pacing. Recently, there has been a renewed interest in studying first price auctions in this context which this paperContinue reading “Pacing Equilibrium in First-Price Auction Markets”

The theory and measurement brief will save you time by delivering summaries of recent applied research papers in Economics & Computation and Causal Inference and its industry applications right to your inbox. Academia and the tech industry produce an ever growing amount of research in economics, computation and causal measurement relevant for designing, building andContinue reading “Introducing the theory and measurement brief”