Lars van der Laan

B313

Padelford Hall, Northeast Stevens Way

Seattle, WA 98103

lvdlaan@uw.edu

About me

I am a fourth-year Ph.D. student in Statistics at the University of Washington, specializing in causal inference and debiased machine learning. I am advised by Marco Carone, PhD and Alex Luedtke, PhD. I also collaborate with Ahmed Alaa at UC Berkeley Computational Precision Health on model calibration and conformal prediction. My research is currently supported by a Netflix Graduate Research Fellowship, through which I work with Nathan Kallus and Aurelien Bibaut. For more detail on my background, please check out my CV.

My research interests encompass a wide range of areas, including semiparametric statistics, statistical learning and calibration, reinforcement learning, debiased machine learning, and causal inference. I am enthusiastic about applying these methodologies to various domains, such as survival and longitudinal data analysis, inference on heterogeneous treatment effects, and personalized decision-making.

For the latest updates on my research, you can follow me on Twitter at @Larsvanderlaan3 and connect with me on LinkedIn. Additionally, you can access all my research publications on my Google Scholar profile. To explore a curated selection of my works, please visit the publications tab.

selected publications

2025

Automatic Debiased Machine Learning for Smooth Functionals of Nonparametric M-Estimands

Lars van der Laan, Aurelien Bibaut, Nathan Kallus, and 1 more author

2025

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We propose a unified framework for automatic debiased machine learning (autoDML) to perform inference on smooth functionals of infinite-dimensional M-estimands, defined as population risk minimizers over Hilbert spaces. By automating debiased estimation and inference procedures in causal inference and semiparametric statistics, our framework enables practitioners to construct valid estimators for complex parameters without requiring specialized expertise. The framework supports Neyman-orthogonal loss functions with unknown nuisance parameters requiring data-driven estimation, as well as vector-valued M-estimands involving simultaneous loss minimization across multiple Hilbert space models. We formalize the class of parameters efficiently estimable by autoDML as a novel class of nonparametric projection parameters, defined via orthogonal minimum loss objectives. We introduce three autoDML estimators based on one-step estimation, targeted minimum loss-based estimation, and the method of sieves. For data-driven model selection, we derive a novel decomposition of model approximation error for smooth functionals of M-estimands and propose adaptive debiased machine learning estimators that are superefficient and adaptive to the functional form of the M-estimand. Finally, we illustrate the flexibility of our framework by constructing autoDML estimators for the long-term survival under a beta-geometric model.
Automatic Double Reinforcement Learning in Semiparametric Markov Decision Processes with Applications to Long-Term Causal Inference

Lars van der Laan, David Hubbard, Allen Tran, and 2 more authors

2025

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Double reinforcement learning (DRL) enables statistically efficient inference on the value of a policy in a nonparametric Markov Decision Process (MDP) given trajectories generated by another policy. However, this approach necessarily requires stringent overlap between the state distributions of the two policies, which is often violated in practice. To relax this requirement and extend DRL, we study efficient inference on linear functionals of the Q-function (of which policy value is a special case) in infinite-horizon, time-invariant MDPs under semiparametric restrictions on the Q-function. These restrictions can alleviate the overlap requirement and lower the efficiency bound, yielding more precise estimates. As an important example, we study the evaluation of long-term value under domain adaptation, given a few short trajectories from the new domain and restrictions on the difference between the domains. This can be applied to long-term causal inference by combining short and scarce experimental data with long and abundant observational data. Our method combines flexible estimates of the Q-function and the Riesz representer of the functional of interest (e.g., the stationary state density ratio for policy value) and is automatic in that it does not require knowledge of the form of the latter - only the functional of interest. To address potential model misspecification bias, we extend the adaptive debiased machine learning (ADML) framework of van der Laan et al. (2023) to construct nonparametrically valid and superefficient estimators that adapt to the functional form of the Q-function. As a special case, we propose a novel adaptive debiased plug-in estimator that uses isotonic-calibrated fitted Q-iteration - a new calibration algorithm for MDPs - to circumvent the computational challenges of estimating debiasing nuisances from min-max objectives.

2024

Automatic doubly robust inference for linear functionals via calibrated debiased machine learning

Lars van der Laan, Alex Luedtke, and Marco Carone

arXiv preprint arXiv:2411.02771, 2024

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In causal inference, many estimands of interest can be expressed as a linear functional of the outcome regression function; this includes, for example, average causal effects of static, dynamic and stochastic interventions. For learning such estimands, in this work, we propose novel debiased machine learning estimators that are doubly robust asymptotically linear, thus providing not only doubly robust consistency but also facilitating doubly robust inference (e.g., confidence intervals and hypothesis tests). To do so, we first establish a key link between calibration, a machine learning technique typically used in prediction and classification tasks, and the conditions needed to achieve doubly robust asymptotic linearity. We then introduce calibrated debiased machine learning (C-DML), a unified framework for doubly robust inference, and propose a specific C-DML estimator that integrates cross-fitting, isotonic calibration, and debiased machine learning estimation. A C-DML estimator maintains asymptotic linearity when either the outcome regression or the Riesz representer of the linear functional is estimated sufficiently well, allowing the other to be estimated at arbitrarily slow rates or even inconsistently. We propose a simple bootstrap-assisted approach for constructing doubly robust confidence intervals. Our theoretical and empirical results support the use of C-DML to mitigate bias arising from the inconsistent or slow estimation of nuisance functions.
Stabilized Inverse Probability Weighting via Isotonic Calibration

Lars van der Laan, Ziming Lin, Marco Carone, and 1 more author

arXiv preprint arXiv:2411.06342, 2024

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Inverse weighting with an estimated propensity score is widely used by estimation methods in causal inference to adjust for confounding bias. However, directly inverting propensity score estimates can lead to instability, bias, and excessive variability due to large inverse weights, especially when treatment overlap is limited. In this work, we propose a post-hoc calibration algorithm for inverse propensity weights that generates well-calibrated, stabilized weights from user-supplied, cross-fitted propensity score estimates. Our approach employs a variant of isotonic regression with a loss function specifically tailored to the inverse propensity weights. Through theoretical analysis and empirical studies, we demonstrate that isotonic calibration improves the performance of doubly robust estimators of the average treatment effect.
Self-Calibrating Conformal Prediction

Lars van der Laan, and Ahmed M. Alaa

The Thirty-eighth Annual Conference on Neural Information Processing Systems, 2024

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In machine learning, model calibration and predictive inference are essential for producing reliable predictions and quantifying uncertainty to support decision- making. Recognizing the complementary roles of point and interval predictions, we introduce Self-Calibrating Conformal Prediction, a method that combines Venn- Abers calibration and conformal prediction to deliver calibrated point predictions alongside prediction intervals with finite-sample validity conditional on these pre- dictions. To achieve this, we extend the original Venn-Abers procedure from binary classification to regression. Our theoretical framework supports analyzing confor- mal prediction methods that involve calibrating model predictions and subsequently constructing conditionally valid prediction intervals on the same data, where the conditioning set or conformity scores may depend on the calibrated predictions. Real-data experiments show that our method improves interval efficiency through model calibration and offers a practical alternative to feature-conditional validity.
Adaptive-TMLE for the Average Treatment Effect based on Randomized Controlled Trial Augmented with Real-World Data

Mark van der Laan, Sky Qiu, and Lars van der Laan

arXiv preprint arXiv:2405.07186, 2024

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We consider the problem of estimating the average treatment effect (ATE) when both randomized control trial (RCT) data and real-world data (RWD) are available. We decompose the ATE estimand as the difference between a pooled-ATE estimand that integrates RCT and RWD and a bias estimand that captures the conditional effect of RCT enrollment on the outcome. We introduce an adaptive targeted minimum loss-based estimation (A-TMLE) framework to estimate them. We prove that the A-TMLE estimator is root-n-consistent and asymptotically normal. Moreover, in finite sample, it achieves the super-efficiency one would obtain had one known the oracle model for the conditional effect of the RCT enrollment on the outcome. Consequently, the smaller the working model of the bias induced by the RWD is, the greater our estimator’s efficiency, while our estimator will always be at least as efficient as an efficient estimator that uses the RCT data only. A-TMLE outperforms existing methods in simulations by having smaller mean-squared-error and 95% confidence intervals. A-TMLE could help utilize RWD to improve the efficiency of randomized trial results without biasing the estimates of intervention effects. This approach could allow for smaller, faster trials, decreasing the time until patients can receive effective treatments.
Combining T-learning and DR-learning: a framework for oracle-efficient estimation of causal contrasts

Lars van der Laan, Marco Carone, and Alex Luedtke

arXiv preprint arXiv:2402.01972, 2024

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We introduce efficient plug-in (EP) learning, a novel framework for the estimation of heterogeneous causal contrasts, such as the conditional average treatment effect and conditional relative risk. The EP-learning framework enjoys the same oracle-efficiency as Neyman-orthogonal learning strategies, such as DR-learning and R-learning, while addressing some of their primary drawbacks, including that (i) their practical applicability can be hindered by loss function non-convexity; and (ii) they may suffer from poor performance and instability due to inverse probability weighting and pseudo-outcomes that violate bounds. To avoid these drawbacks, EP-learner constructs an efficient plug-in estimator of the population risk function for the causal contrast, thereby inheriting the stability and robustness properties of plug-in estimation strategies like T-learning. Under reasonable conditions, EP-learners based on empirical risk minimization are oracle-efficient, exhibiting asymptotic equivalence to the minimizer of an oracle-efficient one-step debiased estimator of the population risk function. In simulation experiments, we illustrate that EP-learners of the conditional average treatment effect and conditional relative risk outperform state-of-the-art competitors, including T-learner, R-learner, and DR-learner. Open-source implementations of the proposed methods are available in our R package hte3.

2023

Adaptive debiased machine learning using data-driven model selection techniques

Lars van der Laan, Marco Carone, Alex Luedtke, and 1 more author

arXiv preprint arXiv:2307.12544, 2023

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Debiased machine learning estimators for nonparametric inference of smooth functionals of the data-generating distribution can suffer from excessive variability and instability. For this reason, practitioners may resort to simpler models based on parametric or semiparametric assumptions. However, such simplifying assumptions may fail to hold, and estimates may then be biased due to model misspecification. To address this problem, we propose Adaptive Debiased Machine Learning (ADML), a nonparametric framework that combines data-driven model selection and debiased machine learning techniques to construct asymptotically linear, adaptive, and superefficient estimators for pathwise differentiable functionals. By learning model structure directly from data, ADML avoids the bias introduced by model misspecification and remains free from the restrictions of parametric and semiparametric models. While they may exhibit irregular behavior for the target parameter in a nonparametric statistical model, we demonstrate that ADML estimators provides regular and locally uniformly valid inference for a projection-based oracle parameter. Importantly, this oracle parameter agrees with the original target parameter for distributions within an unknown but correctly specified oracle statistical submodel that is learned from the data. This finding implies that there is no penalty, in a local asymptotic sense, for conducting data-driven model selection compared to having prior knowledge of the oracle submodel and oracle parameter. To demonstrate the practical applicability of our theory, we provide a broad class of ADML estimators for estimating the average treatment effect in adaptive partially linear regression models.
Causal isotonic calibration for heterogeneous treatment effects

Lars van der Laan, Ernesto Ulloa-Pérez, Marco Carone, and 1 more author

In Proceedings of the 40th International Conference on Machine Learning (ICML), 2023

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We propose causal isotonic calibration, a novel nonparametric method for calibrating predictors of heterogeneous treatment effects. Furthermore, we introduce cross-calibration, a data-efficient variant of calibration that eliminates the need for hold-out calibration sets. Cross-calibration leverages cross-fitted predictors and generates a single calibrated predictor using all available data. Under weak conditions that do not assume monotonicity, we establish that both causal isotonic calibration and cross-calibration achieve fast doubly-robust calibration rates, as long as either the propensity score or outcome regression is estimated accurately in a suitable sense. The proposed causal isotonic calibrator can be wrapped around any black-box learning algorithm, providing robust and distribution-free calibration guarantees while preserving predictive performance.

2022

hal9001: The scalable highly adaptive lasso

Jeremy R Coyle, Nima S Hejazi, Rachael V Phillips, and 2 more authors

2022

R package version 0.4.2

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