Stay tuned – R package coming soon!

In our theory, we show how the estimator’s bias depends on product of imbalance across units, time and bias of the regression adjustment, so that effectively only one of these three needs to be small. This gives flexibility in the choice of unit, time weights and outcome model.

This isn't surprising if it’s common that heterogeneity across units can be approximated with a few unobserved factors and the impact of each factor varies over time. This implies that we shouldn't assign the same weights to units far in time and we should accurately model interactive fixed effects.

We find substantial heterogeneity in performance of common methods. TROP outperforms all competitors in 20 of 21 simulation designs. Empirically additive time and unit effects rarely capture all of the predictable patterns. Instead interactive latent factor structures often fit the data much better

In contrast, TROP embeds all these estimators as special cases, while it learns which components of such estimators are most relevant to accurately predict the counterfactual. We evaluate TROP through a large set of simulation studies calibrated to match real-world applications.

Why this matters: TROP learns the combination of time, unit weights and regression adjustments that most accurately predict the counterfactual as below. Common estimators of causal effects in panel data (DID/TWFE, SC, MC, SDID) rest on different assumptions, but all can fail in applications.

TROP is “triply robust” – error is the product of the errors in the three components: imbalance betw/ treated and control periods, betw/ treated and control units and and misspecification of the regression adjustment (only one needs to perform well)

TROP estimates causal effects in panel models by combining (i) a flexible outcome model (regularized low-rank factors + FE), (ii) unit weights, and (iii) time weights.

And learn more about surrogate indices in the papers by @susanathey.bsky.social, Raj Chetty, Guido Imbens, and Hyunseung Kang.

www.nber.org/papers/w26463

arxiv.org/abs/2006.09676

Learn more about how ratings, measurement, nudges, and dashboards are used to support service quality on online platforms in the paper by @susanathey.bsky.social, Juan Camilo Castillo, & Bharat Chandar

www.nber.org/papers/w33087

Watch @Susan_Athey’s talk on the implications of AI on the organisation of industry and work at #G20SouthAfrica

www.youtube.com/watch?v=okDG...

The talk will cover:

✔️How AI is altering industry dynamics & structures

✔️How these shifts will impact public services such as health and education

✔️How AI market concentration could tax the global economy

✔️Why govt policy will be crucial in shaping AI competition and innovation

Beyond predictions, @keyonv.bsky.social also worked with @gsbsilab.bsky.social to show how these models can be used to make better estimations of important problems, such as the gender wage gap among men and women with the same career histories. Learn more here: bsky.app/profile/gsbs...

Paper: arxiv.org/abs/2409.09894

Analyzing representations tells us where history explains the gap.

Ex: there are two kinds of managers: those who used to be engineers and those who didn’t. The first group gets paid more and has more males than the second.

Models that don’t use history omit this distinction.

We use these methods to estimate wage gaps adjusted for full job history, following the literature on gender wage gaps.

History explains a substantial fraction of the remaining wage gap when compared to simpler methods. But there’s still a lot that history can’t account for.