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, advised by Marco Carone and Alex Luedtke.

My research focuses on causal inference, statistical learning, and semiparametric efficiency theory.

I develop methods for debiased and efficient estimation using modern machine learning tools, including doubly robust inference, simplifying inference via automatic debiasing, inference after model selection, and estimation of heterogeneous treatment effects.

A central theme of my research is bridging calibration—a tool traditionally used in prediction—with causal inference. This includes methods such as causal isotonic calibration for heterogeneous treatment effects, stabilized inverse probability weighting, Bellman calibration for reinforcement learning, and calibrated debiased machine learning.

I also work on distribution-free uncertainty quantification in predictive settings. As a Visiting Student Researcher at UC Berkeley, I collaborate with Ahmed Alaa, with peer-reviewed work presented at NeurIPS and ICML. This includes integrating calibration into conformal prediction and developing generalized Venn–Abers calibration for uncertainty-aware prediction.

I am currently supported by a Netflix Graduate Research Fellowship, and work with Nathan Kallus and Aurélien Bibaut on sequential and long-term causal inference and nonparametric instrumental variables inference.

I’ve applied my research in causal inference and machine learning to problems in both biomedical and technology domains, including internships at Genentech, the Fred Hutchinson Cancer Center, and Netflix. I am also a contributor to the tlverse software ecosystem and consult on software development for TLRevolution.

I have also served as a teaching assistant for graduate-level courses in theoretical statistics and I enjoy mentoring students through research projects.

selected publications

2025

Nonparametric Instrumental Variable Inference with Many Weak Instruments

Lars van der Laan, Nathan Kallus, and Aurélien Bibaut

2025

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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 number of instrument values grows with the sample size. A key motivating example is estimating long-term causal effects in a new experiment with only short-term outcomes, using past experiments to instrument for the effect of short- on long-term outcomes. Here, the assignment to a past experiment serves as the instrument: we have many past experiments but only a limited number of units in each. Since the structural function is nonparametric but constrained by only finitely many moment restrictions, point identification typically fails. To address this, we consider linear functionals of the minimum-norm solution to the moment restrictions, which is always well-defined. As the number of instrument levels grows, these functionals define an approximating sequence to a target functional, replacing point identification with a weaker asymptotic notion suited to discrete instruments. Extending the Jackknife Instrumental Variable Estimator (JIVE) beyond the classical parametric setting, we propose npJIVE, a nonparametric estimator for solutions to linear inverse problems with many weak instruments. We construct automatic debiased machine learning estimators for linear functionals of both the structural function and its minimum-norm projection, and establish their efficiency in the many-weak-instruments regime.
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.
Stabilized Inverse Probability Weighting via Isotonic Calibration

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

In Proceedings of the 3rd Conference on Causal Learning and Reasoning (CLeaR), 2025

To appear

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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.

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.
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.
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

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.