11.1k citations · 16.3k across the 88 of their papers we have counts for
7 papers · 1 filter
On the calibration of survival models with competing risks
Julie Alberge, Tristan Haugomat, Gaël Varoquaux +1
Survival analysis deals with modeling the time until an event occurs, and accurate probability estimates are crucial for decision-making, particularly in the competing-risks settin…
Causal thinking for decision making on Electronic Health Records: why and how
Matthieu Doutreligne, Tristan Struja, Judith Abecassis +3
Accurate predictions, as with machine learning, may not suffice to provide optimal healthcare for every patient. Indeed, prediction can be driven by shortcuts in the data, such as…
Risk ratio, odds ratio, risk difference... Which causal measure is easier to generalize?
Bénédicte Colnet, Julie Josse, Gaël Varoquaux +1
There are many measures to report so-called treatment or causal effects: absolute difference, ratio, odds ratio, number needed to treat, and so on. The choice of a measure, e.g. ab…
Reweighting the RCT for generalization: finite sample error and variable selection
Bénédicte Colnet, Julie Josse, Gaël Varoquaux +1
Randomized Controlled Trials (RCTs) may suffer from limited scope. In particular, samples may be unrepresentative: some RCTs over- or under- sample individuals with certain charact…
Causal effect on a target population: a sensitivity analysis to handle missing covariates
Bénédicte Colnet, Julie Josse, Erwan Scornet +1
Randomized Controlled Trials (RCTs) are often considered the gold standard for estimating causal effect, but they may lack external validity when the population eligible to the RCT…
Causal inference methods for combining randomized trials and observational studies: a review
Bénédicte Colnet, Imke Mayer, Guanhua Chen +6
With increasing data availability, causal effects can be evaluated across different data sets, both randomized controlled trials (RCTs) and observational studies. RCTs isolate the…