He did it before Double Machine Learning I met with professor Mark van der Laan because I think his work is pretty incredible and it sometimes feels like a secret that only a few people know about, especially in industry. 1/ #CausalSky #StatSky #CausalInference

I've advised 15 PhD students—10 were international students. All graduates continue advancing U.S. excellence in research and education. Cutting off this pipeline of talent would be shortsighted.

I had a hard time believing it was as simple as this until Lars taught me how to implement it - calibrate=True and you're done github.com/apoorvalal/a...

Had a great time presenting at #ACIC on doubly robust inference via calibration Calibrating nuisance estimates in DML protects against model misspecification and slow convergence. Just one line of code is all it takes.

link 📈🤖 Nonparametric Instrumental Variable Inference with Many Weak Instruments (Laan, Kallus, Bibaut) We study inference on linear functionals in the nonparametric instrumental variable (NPIV) problem with a discretely-valued instrument under a many-weak-instruments asymptotic regime, where the

I’ll be giving an oral presentation at ACIC in the Advancing Causal Inference session with ML on Wednesday! My talk will be on Automatic Double Reinforcement Learning and long term causal inference! I’ll discuss Markov decision processes, Q-functions, and a new form of calibration for RL!

New preprint with #Netflix out! We study the NPIV problem with a discrete instrument under a many-weak-instruments regime. A key application: constructing confounding-robust surrogates using past experiments as instruments. My mentor Aurélien Bibaut will be presenting a poster at #ACIC2025!

Our work on stabilized inverse probability weighting via calibration was accepted to #CLeaR2025! I gave an oral presentation last week and was honored to receive the Best Paper Award. I’ll be giving a related poster talk at #ACIC on calibration and DML and how it provides doubly robust inference!

I’ll be giving an oral presentation at ACIC in the Advancing Causal Inference session with ML on Wednesday! My talk will be on Automatic Double Reinforcement Learning and long term causal inference! I’ll discuss Markov decision processes, Q-functions, and a new form of calibration for RL!

🚨 Excited about this new paper on Generalized Venn Calibration and conformal prediction! We show that Venn and Venn-Abers can be extended to general losses, and that conformal prediction can be viewed as Venn multicalibration for the quantile loss! #calibration #conformal

Lars van der Laan, Ahmed Alaa Generalized Venn and Venn-Abers Calibration with Applications in Conformal Prediction https://arxiv.org/abs/2502.05676

Your comment also reminds me of this paper where they ensure the estimators solve a certain equation (which I think can be viewed as a kind of balance) using isotonic regression and they show this leads to DR inference: arxiv.org/pdf/2411.02771

Thrilled to share our new paper! We introduce a generalized autoDML framework for smooth functionals in general M-estimation problems, significantly broadening the scope of problems where automatic debiasing can be applied!

link 📈🤖 Automatic Debiased Machine Learning for Smooth Functionals of Nonparametric M-Estimands (Laan, Bibaut, Kallus et al) We propose a unified framework for automatic debiased machine learning (autoDML) to perform inference on smooth functionals of infinite-dimensional M-estimands, defined as

Thrilled to share our new paper! We introduce a generalized autoDML framework for smooth functionals in general M-estimation problems, significantly broadening the scope of problems where automatic debiasing can be applied!

A new Double RL (yes RL) paper by @larsvanderlaan3.bsky.social and colleagues Love this stuff, this is something I was thinking about for a while and great to see a paper on this topic! #CausalSky

Excited to share our work on Double RL and long-term causal inference! This project grew out of my internship at Netflix last summer.

Excited to present "Self-Calibrating Conformal Prediction" at #NeurIPS2024 this afternoon! Join me at the poster session to learn how combining model calibration with predictive inference gives calibrated point predictions and conditionally valid prediction intervals

Mark van der Laan, Sky Qiu, Lars van der Laan Adaptive-TMLE for the Average Treatment Effect based on Randomized Controlled Trial Augmented with Real-World Data https://arxiv.org/abs/2405.07186

Lars van der Laan, Alex Luedtke, Marco Carone Automatic doubly robust inference for linear functionals via calibrated debiased machine learning https://arxiv.org/abs/2411.02771

Lars van der Laan, Ziming Lin, Marco Carone, Alex Luedtke Stabilized Inverse Probability Weighting via Isotonic Calibration https://arxiv.org/abs/2411.06342

Excited to share that our paper "Self-Calibrating Conformal Prediction" with Ahmed Alaa is accepted at #NeurIPS2024! 🚀 We combine model calibration and prediction intervals by integrating Venn-Abers into conformal prediction. #conformal #calibration arxiv.org/pdf/2402.07307