Lars van der Laan

What does ‘biased’ mean here? It would be biased in expectation, since if you were to repeat the experiment many times, some power users would join. If you define your estimator as the empirical mean over non-power users, then it might be unbiased.

I’d be surprised if this actually works in practice, since neural networks are often overfitting (e.g. perfectly fitting labels with double descent), which violates donsker conditions. And, the neural tangent kernel ridge approximation of neural networks has been shown to not hold empirically.

Kernel Debiased Plug-in Estimation: Simultaneous, Automated Debiasing without Influence Functions for Many Target Parameters When estimating target parameters in nonparametric models with nuisance parameters, substituting the unknown nuisances with nonparametric estimators can introduce ``plug-in bias.'' Traditional methods...

Looks like they are assuming the neural network can be approximated by ridge regression in an RKHS (which seems strong in practice). Under this approximation, plug-in efficiency follows from fairly standard results on undersmoothed ridge regression; see, e.g. arxiv.org/abs/2306.08598

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...

Calibrate your outcome predictions and propensities using isotonic regression as follows:

mu_hat <- as.stepfun(isoreg(mu_hat, Y))(mu_hat)

pi_hat <- as.stepfun(isoreg(pi_hat, A))(pi_hat)

(Or use the isoreg_with_xgboost function given in the paper, which I recommend)

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!

Link to paper:

arxiv.org/pdf/2501.06926

This work is a result of my internship at Netflix over the summer and is joint with Aurelien Bibaut and Nathan Kallus.

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!

Inference for smooth functionals of M-estimands in survival models, like regularized coxPH and the beta-geometric model (see our experiments section) are one application of this approach.

By targeting low dimensional summaries, there is no need to establish asymptotic normality of the entire infinite dimensional M-estimator (which isn’t possible in general). It allows for the use of ML and regularization to estimate it, and valid inference via a one step bias correction.

If you’re willing to consider smooth functionals of the infinite dimensional M-estimand, then there is a general theory for inference, where the sandwich variance estimator now involves the derivative of the loss and a Riesz representer of the functional.

Working paper:
arxiv.org/pdf/2501.11868

The motivation should have been something like a confounder that is somewhat predictive of both the treatment and outcome might be more important to adjust for then a variable that is super predictive of the outcome but doesn’t predict treatment. TR might help give more importance to such variables

One could have given an analogous theorem saying that E[Y | T, X] is a sufficient deconfounding score and argued that one should only adjust for features predictive of the outcome. So yeah I think it’s wrong/poorly phrased

The OP’s approach is based on the conditional probability of Y given the treatment is intervened upon and set to some value. But, they don’t seem to define what this means formally, which is exactly what potential outcomes/NPSEM achieve.

The second stage coefficients are the estimand (identifying the structural coefficients/treatment effect). The first stage coefficients are nuisances, and typically not of direct interest.

The paper "Generalized Venn and Venn-Abers Calibration with Applications in Conformal Prediction" by Lars van der Laan and Ahmed Alaa introduces a comprehensive framework that extends Venn and Venn-Abers calibration methods to a broad range of prediction tasks and loss functions.

🚨 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

Great discussion! there we use that balancing conditional on the propensity score (or Riesz rep more generally) is sufficient for ipw and DR inference.

To add to the discussion this paper connects balancing with debiasing in aipw

arxiv.org/pdf/2304.14545

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