📊 Most cited sources in A/B Testing

A hand-curated leaderboard from Ron Kohavi — the researcher behind much of the modern A/B testing literature (ex-Microsoft, Amazon, Airbnb) — ranking the most-cited work in A/B testing / Online Controlled Experiments by citations per year, with a strict cutoff of 10+ cites/yr. Scoped deliberately to controlled experiments, not causal inference in general. Each paper's citation count is tracked over time (snapshots back to 2022), with newcomers and fast risers flagged.

🏆 The top of the list — start here:
• Controlled experiments on the web: survey and practical guide — Kohavi, Longbotham, Sommerfield, Henne (2009) — the classic, ~62 cites/yr
• Trustworthy Online Controlled Experiments: A Practical Guide to A/B Testing — Kohavi, Tang, Xu (2020) — the field's standard reference book (57k+ ResearchGate reads)
• Online Controlled Experiments and A/B Testing — Kohavi, Longbotham (2016)
• Experimentation and Start-up Performance: Evidence from A/B Testing — Koning, Hasan, Chatterji (2022)

🔬 Deeper cuts worth knowing:
• A causal test of the strength of weak ties — Rajkumar, Saint-Jacques, Bojinov, Brynjolfsson, Aral (2022)
• Exact p-Values for Network Interference — Athey, Eckles, Imbens (2018)
• Design and Analysis of Experiments in Networks: Reducing Bias from Interference — Eckles, Karrer, Ugander (2017)
• The surrogate index: combining short-term proxies to estimate long-term effects — Athey, Chetty, Imbens, Kang (2019)
• Design and analysis of switchback experiments — Bojinov, Simchi-Levi, Zhao (2022)
• Online controlled experiments at large scale — Kohavi, Deng, Frasca, Walker, Xu, Pohlmann (2013)

Link: Google Spreadsheet

Navigational hashtags: #armknowledgesharing #armtutorials
General hashtags: #abtesting #experimentation #datascience #causalinference #statistics

@data_science_weekly