When A/B experiments are unavailable or ineffective, Propensity Score Matching (PSM) offers a rigorous causal inference method by estimating treatment probabilities and matching treated and control units, allowing reliable evaluation of intervention effects across various real‑world scenarios.
The article explains Propensity Score Matching as a causal inference method, detailing treatment effect concepts, required assumptions, score estimation, various matching algorithms, SQL implementation, quality assessment metrics, and how to estimate ATT using Difference-in-Differences, while outlining workflow steps, trade-offs, and alternatives.
This article analyses the unique challenges of stranger‑social platforms such as Tinder and Tantan, exploring business models, user behavior, network effects, gender dynamics, data collection, algorithmic matching, risk control, and system architecture to guide product strategy and optimization.
This article introduces the Tree‑based Deep Match (TDM) framework, which uses a novel max‑heap tree structure to enable efficient, hierarchical retrieval over massive candidate sets, allowing any advanced deep learning model to improve matching accuracy, recall, and novelty in industrial recommendation systems.
