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- Add hdm and glmnet libraries for regularized regression - Implement double_lasso_ps() function using Belloni et al. (2014) double-lasso variable selection for propensity score estimation - Add did_double_lasso_ps() wrapper for DID estimation with PS weights - Expand Section 6 with detailed methodology description (6.1-6.5) covering Borgschulte & Vogler (2020) approach - Update replication code with three-panel comparison: Standard DID, Double-Lasso PS DID, and SDID methods - Add propensity score distribution diagnostics and visualization - Add references for Belloni et al. (2014) and Borgschulte & Vogler (2020) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
- R_weighted/: Modified synthdid package with weighting support - vignettes/synthdid_weighted.Rmd: Documentation vignette Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
bootstrap procedure
implemented it yet.
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Hi John!
I updated the writing, added in a section on inference, and started editing section 6. I think the population weighted SDID is our primary contribution for this paper -- so I think we need to emphasize it and hype it up! While I think the weighted average of SDID etc runs are interesting, I think they might distract from our contribution. So I propose we drop those from this paper and if we want, we can explore them in another one. If we're smart, we might be able to get a few papers out of this.