Hi all,
Our next virtual meeting will be at 12pm (EST) Wednesday, February 3
(tomorrow), where we will hear Alex Tarr (Princeton University) presents
research on "Estimating Average Treatment Effects with Support Vector
Machines."
*Abstract*:
Support vector machine (SVM) is one of the most popular classification
algorithms in the machine learning literature. We demonstrate that SVM can
be used to balance covariates and estimate average causal effects under the
unconfoundedness assumption. We show that the SVM cost parameter controls
the trade-off between covariate balance and subset size, and as a result,
existing SVM regularization path algorithms can be used to compute the
balance-sample size frontier. We then characterize the bias of causal
effect estimation arising from this tradeoff, connecting the proposed SVM
procedure to the existing kernel balancing methods. Finally, we conduct
simulation and empirical studies to evaluate the performance of the
proposed methodology and find that SVM is competitive with the
state-of-the-art covariate balancing methods.
*Zoom link*:
https://harvard.zoom.us/j/97787602526?pwd=Uzh3bVVVS0F4TEVYQTJlV3BQNjcydz09
*Schedule of the workshop*:
https://projects.iq.harvard.edu/applied.stats.workshop-gov3009
Looking forward to seeing you all tomorrow!
Best,
Soichiro
--
Soichiro Yamauchi
PhD candidate
Harvard University
URL:
https://soichiroy.github.io/