Venice School of Management’s Post

This is a great paper and I agree with Paul Hünermund but I’ll go even further. I don’t like myself listing the covariates with estimated coefficients in the same table as a treatment variable identified as causal precisely because of what Paul says. The controls are to satisfy thinfs like the backdoor criterion, and the coefficients are not causal probably but if you list them in the table witu the causal variable then what’s to stop someone from drawing that conclusion? Why are those coefficient helpful? The table is to present the causal parameter estimate that presumably the covariates helped identify but they are not themselves useful or correct measures of any parameter of interest. Putting them therefore in the table suffers from two glaring grammatical errors: 1) your reader has no way to know which is causal and which isn’t and why 2) any number in a table in principle should be discussed but by definition, you have far more covariates than treatment variable so they’ll crowd out the discussion of the real research question I don’t report the r squared anymore for causal estimation because we aren’t predicting the outcome; we are predicting the counterfactual and comparing it to the relevant groups realized outcomes. And I dont report covariates anymore; I simply indicate that they were included (usually in the footnote of the table) and clearly in the text.

First review is in: https://lnkd.in/eafpHNKc

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It was a great evening for ACT prep with a few of the Mobile Kappa League scholars! We focused on the reading and science sections of the ACT. The scholars gave AWESOME feedback on each section. Damaurion shared a few tips that will help him on the ACT science section. 1.) don’t focus on the passages 2.) focus on the data first and analyze it 3.) pull your answers from the tables & figures

A fun paper to publish with Seung Kyeom Kim, Nick Stergiou, and Aaron Likens, Ph.D., titled Pattern analysis using lower body human walking data to identify the gaitprint! More person identification to follow in the future! https://lnkd.in/gjs5dmW8

https://lnkd.in/eH-JrRrY Deep Dive: Transforming Text into Knowledge Graphs with LLM MinYang C.

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The new FERC insight is out today! Catch up on what's new at FERC!