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ML/Statistics

A collection of 143 issues

Unmatched: The Problem With Comparing Matching Methods

In many matching papers, the key claim proceeds as follows: our matching method is better than others because on this set of contrived data, treatment effect estimates are closest to those from the ‘gold standard’ (experimental evidence). Let’s side-step concerns related to an important point: evidence that a method

Optimal Recruitment For Experiments: Using Pair-Wise Matching Distance to Guide Recruitment

Pairwise matching before randomization reduces s.e. (see below, for instance). Generally, the strategy is used to create balanced control and treatment groups from available observations. But we can use the insight for optimal sample recruitment, especially in cases where we have a large panel of respondents with baseline data,

AFib: Apple Watch Did Not Increase Atrial Fibrillation Diagnoses

A new paper purportedly shows that the release of the Apple Watch 2018, which supported the ECG app, did not cause an increase in AFib diagnoses (mean = −0.008). They make the claim based on 60M visits from 1270 practices across 2 years. Here are some things to think about:

Predicting Reliable Respondents

Setting aside concerns about sampling, the quality of survey responses on popular survey platforms is abysmal (see here and here). Both insincere and inattentive respondents are at issue. A common strategy for identifying inattentive respondents is to use attention checks. However, many of these attention checks stick out like sore

Interacting With Human Decisions

In sport, as in life, luck plays a role. For instance, in cricket, there is a toss at the start of the game. And the team that wins the toss wins the game 3% more often. The estimate of the advantage from winning the toss, however, is likely an underestimate

Lost Years: From Lost Lives to Life Lost

The mortality rate is puzzling to mortals. A better number is the expected number of years lost. (A yet better number would be quality-adjusted years lost.) To make it easier to calculate the expected years lost, Suriyan and I developed a Python package that uses the SSA actuarial data and