Thanks! Please send us suggestions if you have any!

We also highlighted the need to use the leave-one-out approach to obtain pretrend estimates, thanks to Zikai and Anton's cautionary note: osf.io/ngr3d_v1/ @astrezh.bsky.social

In this new version, we fixed many bugs and introduced more features.

Check it out: yiqingxu.org/packages/fect/

Special thanks to Ziyi, Rivka, and Tianzhu for their incredible work and dedication. More features are on the way!

Hi Vincent, thank you! Yes, we're working on a user manual that will incorporate these datasets. It will take a few weeks!

7/ We have prototypes for all the algorithms used in the Element and will soon roll them out in **interflex** for R.

Comments and suggestions are more than welcome!

6/ Through simulations & many empirical examples, we find AIPW-Lasso & PO-Lasso outperforming competitors with political science data, while DML requires much larger samples to do better.

5/ We then turn to double machine learning (DML), incorporating modern learners such as neural nets, random forests, and HGB to estimate nuisance parameters and construct signals.

4/ The core of the Element is robust estimation strategies.

First, we adapt AIPW-Lasso & Partialing-Out Lasso, both w/ basis expansion & Lasso double selection.

We walk through signal construction step-by-step to aid intuition. For smoothing, we support both kernel and B-spline regressions.

3/ We start by defining the estimand, the CME, and presenting main identification results in the discrete-covariate case.

We then review & improve the semiparametric kernel estimator. The improvements include fully moderated models, adaptive kernels, and uniform confidence intervals.

2/ We prepared it for Cambridge Element & previewed parts of it in our response to a blog post last month.

Scholars are often interested in how treatment effects vary with a moderating variable (example below).

We hope this will serve as a useful reference for this common task down the road.

5/ We then turn to double machine learning (DML), incorporating modern learners such as neural nets, random forests, and HGB to estimate nuisance parameters and construct signals.

4/ The core of the Element is robust estimation.

First, we adapt AIPW-Lasso & partialing-out Lasso (PO-Lasso), both w/ basis expansion & Lasso double selection.

We walk through signal construction step by step to aid intuition. For smoothing, we support both kernel and B-spline regressions.

3/ We start by defining the estimand, the CME, and presenting the main identification result.

We then review & improve the semiparametric kernel estimator by introducing fully moderated models, adaptive kernels, and uniform confidence intervals.

We prepared this for Cambridge Element and previewed parts of it in our response to a blog post last month.

Scholars are often interested in how treatment effects vary with a moderating variable (example below). We hope this Element will serve as a useful reference for such tasks down the road.

Hah, thanks. You're the reason I'm here, Guilherme

8/ Finally, we thank Professor Simonsohn for his thoughtful critique, which brings renewed attention to the estimation and testing of conditional relationships.

w/ Jens Hainmueller, Jiehan Liu, Ziyi Liu & Jonathan Mummolo

7/ Based on the discussion, we propose a set of recommendations, starting from clearly stating the quantity of interest and the key ID & modeling assumptions.

We provide software support for various estimation strategies: yiqingxu.org/packages/int...