Lars van der Laan

Seattle, WA

lvdlaan@uw.edu

About me

I am a postdoctoral scholar in the Department of Management Science and Engineering at Stanford University, working under Professor Vasilis Syrgkanis and supported by Netflix Research. I completed my Ph.D. in Statistics at the University of Washington in June 2026, advised by Marco Carone and Alex Luedtke, during which I was supported by a Netflix Graduate Research Fellowship.

My research focuses on causal inference, semiparametric statistics, and reinforcement learning. I develop methods for debiased and efficient estimation with machine learning, including calibrated DML for doubly robust inference, adaptive DML for selective inference, automatic DML for M-estimation, and efficient plug-in learning for estimating heterogeneous treatment effects.

I collaborate with Nathan Kallus and Aurélien Bibaut at Netflix. More broadly, my work spans long-term causal inference, nonparametric instrumental variables inference, inverse reinforcement learning and dynamic discrete choice models, and value estimation in offline reinforcement learning, including Fitted Q Evaluation and Fitted Q Iteration without Bellman completeness.

Another theme of my research is calibration: adapting post-hoc tools from predictive modeling to causal inference and dynamic decision-making. This includes causal isotonic calibration for heterogeneous treatment effect estimation, calibration of inverse probability weighting estimators, Bellman calibration for offline reinforcement learning, calibrated debiased machine learning, and calibrated prediction-powered inference. I have also worked on calibration for predictive uncertainty quantification and conformal prediction.

Beyond methodology, I apply these ideas in biomedical and technology settings through research internships at Genentech, the Fred Hutchinson Cancer Center, and Netflix. I also care about teaching and mentorship. I recently wrote a guide on empirical risk minimization.

selected publications

2026

A Researcher’s Guide to Empirical Risk Minimization

Lars van der Laan

arXiv preprint arXiv:2602.21501, 2026

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This guide provides a reference for high-probability regret bounds in empirical risk minimiza- tion (ERM). The presentation is modular: we begin with intuition and general proof strategies, then state broadly applicable guarantees under high-level conditions and provide tools for verify- ing them for specific losses and function classes. We emphasize that many ERM rate derivations can be organized around a three-step recipe—a basic inequality, a uniform local concentration bound, and a fixed-point argument—which yields regret bounds in terms of a critical radius, de- fined via localized Rademacher complexity, under a mild Bernstein-type variance–risk condition (Bartlett et al., 2005; Wainwright, 2019). To make these bounds concrete, we upper bound the critical radius using local maximal inequalities and metric-entropy integrals, thereby recovering familiar rates for VC-subgraph, Sobolev/H¨older, and bounded-variation classes. We also study ERM with nuisance components—including weighted ERM and Neyman- orthogonal losses—as they arise in causal inference, missing data, and domain adaptation. Fol- lowing Foster and Syrgkanis (2023), we highlight that these problems often admit regret-transfer bounds linking regret under an estimated loss to population regret under the target loss. These bounds typically decompose the regret into (i) statistical error under the estimated loss and (ii) approximation error due to nuisance estimation. Under sample splitting or cross-fitting, the first term can be controlled using standard fixed-loss ERM regret bounds, while the second depends only on nuisance-estimation accuracy. As a novel contribution, we also treat the in- sample regime, in which the nuisances and the ERM are fit on the same data, deriving regret bounds and showing that fast oracle rates remain attainable under suitable smoothness and Donsker-type conditions.
Calibeating Prediction-Powered Inference

Lars van der Laan, and Mark van der Laan

arXiv preprint arXiv:2604.21260, 2026

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We study semisupervised mean estimation with a small labeled sample, a large unlabeled sample, and a black-box prediction model whose output may be miscalibrated. A standard approach in this setting is augmented inverse-probability weighting (AIPW) [Robins et al., 1994], which protects against prediction-model misspecification but can be inefficient when the prediction score is poorly aligned with the outcome scale. We introduce Calibrated Prediction-Powered Inference, which post-hoc calibrates the prediction score on the labeled sample before using it for semisupervised estimation. This simple step requires no retraining and can improve the original score both as a predictor of the outcome and as a regression adjustment for semisupervised inference. We study both linear and isotonic calibration. For isotonic calibration, we establish first-order optimality guarantees: isotonic post-processing can improve predictive accuracy and estimator efficiency relative to the original score and simpler post-processing rules, while no further post-processing of the fitted isotonic score yields additional first-order gains. For linear calibration, we show first-order equivalence to PPI++. We also clarify the relationship among existing estimators, showing that the original PPI estimator is a special case of AIPW and can be inefficient when the prediction model is accurate, while PPI++ is AIPW with empirical efficiency maximization [Rubin et al., 2008]. In simulations and real-data experiments, our calibrated estimators often outperform PPI and are competitive with, or outperform, AIPW and PPI++.

2025

Efficient Inference for Inverse Reinforcement Learning and Dynamic Discrete Choice Models

Lars van der Laan, Aurelien Bibaut, and Nathan Kallus

arXiv preprint arXiv:2512.24407, 2025

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Inverse reinforcement learning (IRL) and dynamic discrete choice (DDC) models explain sequential decision-making by recovering reward functions that rationalize observed behaviour. Flexible IRL methods typically rely on machine learning but provide no guarantees for valid inference, while classical DDC approaches impose restrictive parametric specifications and often require repeated dynamic programming. We develop a semiparametric framework for debiased inverse reinforcement learning that yields statistically efficient inference for a broad class of reward-dependent functionals in maximum-entropy IRL and Gumbel-shock DDC models. We show that the log–behaviour policy acts as a pseudo-reward that point-identifies policy value differences and, under a simple normalization, the reward itself. We then formalize these tar- gets—including policy values under known and counterfactual softmax policies and functionals of the normalized reward—as smooth functionals of the behaviour policy and transition ker- nel, establish pathwise differentiability, and derive their efficient influence functions. Building on this characterization, we construct automatic debiased machine-learning estimators that al- low flexible nonparametric estimation of nuisance components while achieving √n-consistency, asymptotic normality, and semiparametric efficiency. Our framework extends classical inference for DDC models to nonparametric rewards and modern machine-learning tools, providing a unified and computationally tractable approach to statistical inference in IRL.
Fitted Q Evaluation without Bellman Completeness via Stationary Weighting

Lars van der Laan, and Nathan Kallus

arXiv preprint arXiv:2512.23805, 2025

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Fitted Q-evaluation (FQE) is a central method for off-policy evaluation in reinforcement learning, but it generally requires Bellman completeness: that the hypothesis class is closed under the evaluation Bellman operator. This requirement is challenging because enlarging the hypothesis class can worsen completeness. We show that the need for this assumption stems from a fundamental norm mismatch: the Bellman operator is gamma-contractive under the stationary distribution of the target policy, whereas FQE minimizes Bellman error under the behavior distribution. We propose a simple fix: reweight each regression step using an estimate of the stationary density ratio, thereby aligning FQE with the norm in which the Bellman operator contracts. This enables strong evaluation guarantees in the absence of realizability or Bellman completeness, avoiding the geometric error blow-up of standard FQE in this setting while maintaining the practicality of regression-based evaluation.
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.
Semiparametric Double Reinforcement Learning with Applications to Long-Term Causal Inference

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

2025

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Long-term causal effects often must be estimated from short-term data due to limited follow-up in healthcare, economics, and online platforms. Markov Decision Processes (MDPs) provide a natural framework for capturing such long-term dynamics through sequences of states, actions, and rewards. Double Reinforcement Learning (DRL) enables efficient inference on policy values in MDPs, but nonparametric implementations require strong intertemporal overlap assumptions and often exhibit high variance and instability. We propose a semiparametric extension of DRL for efficient inference on linear functionals of the Q-function–such as policy values–in infinite-horizon, time-homogeneous MDPs. By imposing structural restrictions on the Q-function, our approach relaxes the strong overlap conditions required by nonparametric methods and improves statistical efficiency. Under model misspecification, our estimators target the functional of the best-approximating Q-function, with only second-order bias. We provide conditions for valid inference using sieve methods and data-driven model selection. A central challenge in DRL is the estimation of nuisance functions, such as density ratios, which often involve difficult minimax optimization. To address this, we introduce a novel plug-in estimator based on isotonic Bellman calibration, which combines fitted Q-iteration with an isotonic regression adjustment. The estimator is debiased without requiring estimation of additional nuisance functions and reduces high-dimensional overlap assumptions to a one-dimensional condition. Bellman calibration extends isotonic calibration–widely used in prediction and classification–to the MDP setting and may be of independent interest.
Stabilized Inverse Probability Weighting via Isotonic Calibration

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

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

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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.
Inverse Reinforcement Learning Using Just Classification and a Few Regressions

Lars van der Laan, Nathan Kallus, and Aurélien Bibaut

arXiv preprint arXiv:2509.21172, 2025

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Inverse reinforcement learning (IRL) aims to explain observed behavior by uncovering an underlying reward. In the maximum-entropy or Gumbel-shocks-to-reward frameworks, this amounts to fitting a reward function and a soft value function that together satisfy the soft Bellman consistency condition and maximize the likelihood of observed actions. While this perspective has had enormous impact in imitation learning for robotics and understanding dynamic choices in economics, practical learning algorithms often involve delicate inner-loop optimization, repeated dynamic programming, or adversarial training, all of which complicate the use of modern, highly expressive function approximators like neural nets and boosting. We revisit softmax IRL and show that the population maximum-likelihood solution is characterized by a linear fixed-point equation involving the behavior policy. This observation reduces IRL to two off-the-shelf supervised learning problems: probabilistic classification to estimate the behavior policy, and iterative regression to solve the fixed point. The resulting method is simple and modular across function approximation classes and algorithms. We provide a precise characterization of the optimal solution, a generic oracle-based algorithm, finite-sample error bounds, and empirical results showing competitive or superior performance to MaxEnt IRL.

2024

Doubly robust inference via calibration

Lars van der Laan, Alex Luedtke, and Marco Carone

arXiv preprint arXiv:2411.02771, 2024

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Doubly robust estimators are widely used for estimating average treatment effects and other linear summaries of regression functions. While consistency requires only one of two nuisance functions to be estimated consistently, asymptotic normality typically require sufficiently fast convergence of both. In this work, we correct this mismatch: we show that calibrating the nuisance estimators within a doubly robust procedure yields doubly robust asymptotic normality for linear functionals. We introduce a general framework, calibrated debiased machine learning (calibrated DML), and propose a specific estimator that augments standard DML with a simple isotonic regression adjustment. Our theoretical analysis shows that the calibrated DML estimator remains asymptotically normal if either the regression or the Riesz representer of the functional is estimated sufficiently well, allowing the other to converge arbitrarily slowly or even inconsistently. We further propose a simple bootstrap method for constructing confidence intervals, enabling doubly robust inference without additional nuisance estimation. In a range of semi-synthetic benchmark datasets, calibrated DML reduces bias and improves coverage relative to standard DML. Our method can be integrated into existing DML pipelines by adding just a few lines of code to calibrate cross-fitted estimates via isotonic regression.
Self-Calibrating Conformal Prediction

Lars van der Laan, and Ahmed M. Alaa

The Thirty-eighth Annual Conference on Neural Information Processing Systems (NeurIPS), 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.