Lars van der Laan
@larsvanderlaan3.bsky.social
Ph.D. Student @uwstat; Research fellowship @Netflix; visiting researcher @UCJointCPH; M.A. @UCBStatistics - machine learning; calibration; semiparametrics; causal inference.
https://larsvanderlaan.github.io
https://larsvanderlaan.github.io
Reposted by Lars van der Laan
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 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
September 12, 2025 at 8:29 AM
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 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
Reposted by Lars van der Laan
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.
May 23, 2025 at 3:36 AM
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.
Reposted by Lars van der Laan
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...
github.com/apoorvalal/a...
May 19, 2025 at 12:59 AM
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...
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.
Calibrating nuisance estimates in DML protects against model misspecification and slow convergence.
Just one line of code is all it takes.
May 19, 2025 at 12:02 AM
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.
Calibrating nuisance estimates in DML protects against model misspecification and slow convergence.
Just one line of code is all it takes.
Reposted by Lars van der Laan
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
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
May 13, 2025 at 4:43 PM
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
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
Reposted by Lars van der Laan
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!
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!
May 12, 2025 at 6:09 PM
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!
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!
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!
Lars van der Laan, Nathan Kallus, Aur\'elien Bibaut
Nonparametric Instrumental Variable Inference with Many Weak Instruments
https://arxiv.org/abs/2505.07729
Nonparametric Instrumental Variable Inference with Many Weak Instruments
https://arxiv.org/abs/2505.07729
May 13, 2025 at 10:43 AM
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 a related poster talk at #ACIC on calibration and DML and how it provides doubly robust inference!
Lars van der Laan, Ziming Lin, Marco Carone, Alex Luedtke
Stabilized Inverse Probability Weighting via Isotonic Calibration
https://arxiv.org/abs/2411.06342
Stabilized Inverse Probability Weighting via Isotonic Calibration
https://arxiv.org/abs/2411.06342
May 12, 2025 at 6:33 PM
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 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!
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!
May 12, 2025 at 6:09 PM
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!
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
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
Generalized Venn and Venn-Abers Calibration with Applications in Conformal Prediction
https://arxiv.org/abs/2502.05676
February 11, 2025 at 6:37 PM
🚨 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
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
Reposted by Lars van der Laan
Lars van der Laan, Ahmed Alaa
Generalized Venn and Venn-Abers Calibration with Applications in Conformal Prediction
https://arxiv.org/abs/2502.05676
Generalized Venn and Venn-Abers Calibration with Applications in Conformal Prediction
https://arxiv.org/abs/2502.05676
February 11, 2025 at 8:10 AM
Lars van der Laan, Ahmed Alaa
Generalized Venn and Venn-Abers Calibration with Applications in Conformal Prediction
https://arxiv.org/abs/2502.05676
Generalized Venn and Venn-Abers Calibration with Applications in Conformal Prediction
https://arxiv.org/abs/2502.05676
Reposted by Lars van der Laan
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
arxiv.org/pdf/2411.02771
arxiv.org
January 25, 2025 at 3:28 PM
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
arxiv.org/pdf/2411.02771
Reposted by Lars van der Laan
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!
Lars van der Laan, Aurelien Bibaut, Nathan Kallus, Alex Luedtke
Automatic Debiased Machine Learning for Smooth Functionals of Nonparametric M-Estimands
https://arxiv.org/abs/2501.11868
Automatic Debiased Machine Learning for Smooth Functionals of Nonparametric M-Estimands
https://arxiv.org/abs/2501.11868
January 22, 2025 at 1:54 PM
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!
Reposted by Lars van der Laan
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
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
January 22, 2025 at 5:17 PM
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
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!
Lars van der Laan, Aurelien Bibaut, Nathan Kallus, Alex Luedtke
Automatic Debiased Machine Learning for Smooth Functionals of Nonparametric M-Estimands
https://arxiv.org/abs/2501.11868
Automatic Debiased Machine Learning for Smooth Functionals of Nonparametric M-Estimands
https://arxiv.org/abs/2501.11868
January 22, 2025 at 1:54 PM
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!
Reposted by Lars van der Laan
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
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.
Lars van der Laan, David Hubbard, Allen Tran, Nathan Kallus, Aur\'elien Bibaut
Automatic Double Reinforcement Learning in Semiparametric Markov Decision Processes with Applications to Long-Term Causal Inference
https://arxiv.org/abs/2501.06926
Automatic Double Reinforcement Learning in Semiparametric Markov Decision Processes with Applications to Long-Term Causal Inference
https://arxiv.org/abs/2501.06926
January 14, 2025 at 5:54 PM
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
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.
Lars van der Laan, David Hubbard, Allen Tran, Nathan Kallus, Aur\'elien Bibaut
Automatic Double Reinforcement Learning in Semiparametric Markov Decision Processes with Applications to Long-Term Causal Inference
https://arxiv.org/abs/2501.06926
Automatic Double Reinforcement Learning in Semiparametric Markov Decision Processes with Applications to Long-Term Causal Inference
https://arxiv.org/abs/2501.06926
January 14, 2025 at 2:06 PM
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
December 12, 2024 at 4:36 PM
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
Reposted by Lars van der Laan
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
Adaptive-TMLE for the Average Treatment Effect based on Randomized Controlled Trial Augmented with Real-World Data
https://arxiv.org/abs/2405.07186
May 14, 2024 at 1:03 PM
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
Adaptive-TMLE for the Average Treatment Effect based on Randomized Controlled Trial Augmented with Real-World Data
https://arxiv.org/abs/2405.07186
Reposted by Lars van der Laan
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
Automatic doubly robust inference for linear functionals via calibrated debiased machine learning
https://arxiv.org/abs/2411.02771
November 6, 2024 at 5:02 AM
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
Automatic doubly robust inference for linear functionals via calibrated debiased machine learning
https://arxiv.org/abs/2411.02771
Reposted by Lars van der Laan
Lars van der Laan, Ziming Lin, Marco Carone, Alex Luedtke
Stabilized Inverse Probability Weighting via Isotonic Calibration
https://arxiv.org/abs/2411.06342
Stabilized Inverse Probability Weighting via Isotonic Calibration
https://arxiv.org/abs/2411.06342
November 12, 2024 at 5:02 AM
Lars van der Laan, Ziming Lin, Marco Carone, Alex Luedtke
Stabilized Inverse Probability Weighting via Isotonic Calibration
https://arxiv.org/abs/2411.06342
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
We combine model calibration and prediction intervals by integrating Venn-Abers into conformal prediction. #conformal #calibration
arxiv.org/pdf/2402.07307
arxiv.org
November 14, 2024 at 2:57 AM
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
We combine model calibration and prediction intervals by integrating Venn-Abers into conformal prediction. #conformal #calibration
arxiv.org/pdf/2402.07307