Algorithmic Advances For The Design And Analysis Of Randomized Control Trials

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" summary

Formalizes a robustness–balance trade-off for covariate balancing and constructs a randomized design that provably navigates that trade-off near-optimally using linear algebra and discrepancy-theory ideas; characterizes which variance bounds are estimable under complex designs and interference and gives convex-programming algorithms to pick the least conservative variance bounds; for bipartite experiments gives linear-exposure estimators, derives unbiased variance estimators and asymptotically valid confidence intervals, and proposes a cluster-based weighted correlation clustering design.

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" chapters (8)

1. 0:00Intro
2. 1:00About Me
3. 2:01My Dissertation Work
4. 2:57Problem
5. 3:11Balancing Covariates
6. 5:25Variance Estimation
7. 7:45Bipartite Experiments
8. 10:03Here at Simons...

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