@mark_vdlaan@topepos The handbook includes code examples and problems motivated by three different real-world data sets: https://t.co/G42K1gHarY. We'd be happy to discuss more too!
@mark_vdlaan@topepos Our handbook includes in-depth introductions to both super learning and targeted learning of causal effects with inference, including both classical causal parameters (e.g., average treatment effect) and modern approaches (based on optimal dynamic and stochastic interventions).
Just to clarify: @mark_vdlaan ‘s lecture is about estimation using targeted learning, typically applied in causal inference, but after identification. Nevertheless, it’s going to be epic! Sold out, but live-streamed #epitwitter
Asked for a comparison of TMLE and double machine learning, this blog post comparing the two approaches in historical context came together quickly https://t.co/veiRrANybW #causalinference#statstwitter#epitwitter#machinelearning
Check out a new blog post from @laan12345 on obtaining inference for prediction quantities (i.e., prediction intervals) using Targeted Learning: https://t.co/YyJdC1XcvL #causalinference#statstwitter#EpiTwitter
Check out a new blog post from @laan12345 on the applications of Targeted Learning in infectious disease research with networked units: https://t.co/sdv4xK8oKp #causalinference#statstwitter#EpiTwitter
Check out a new blog post from @laan12345 on adaptive algorithm library selection for constructing ensemble models with the Super Learner algorithm: https://t.co/QaeLe2t5ne #causalinference#statstwitter#EpiTwitter
Check out a new blog post from @laan12345 on the essential differences between one-step estimators and TML estimators: https://t.co/pYUCjGxkxO #causalinference#statstwitter#EpiTwitter
Check out a new blog post from @laan12345 on dealing with missing data when using Targeted Learning: https://t.co/z8Kia54O7C #causalinference#statstwitter#EpiTwitter
@ildiazm @jon_y_huang @LauraBBalzer@Tfeend@ProfMattFox There’s also an open handbook we’re writing (https://t.co/oC7dtIQXP7) and recent workshop materials (https://t.co/IjUFP5hUFO) with software usage examples. Please let us know how best we can improve these tools!
“Individual men are insoluble puzzles, but in aggregate they become a mathematical certainty. You can't foretell what 1 man will do, but you can say what an avg # will be up to. Individuals vary but %s remain constant. So says the statistician.”
- Sherlock Holmes #statsquotes