Discussion of “On Nearly Assumption-Free Tests of Nominal Confidence Interval Coverage for Causal Parameters Estimated by Machine Learning”

An increasing number of recent studies suggest doubly robust estimators with cross-fitting should be used when estimating causal effects with machine learning methods. However, existing programs that implement doubly robust estimators do not all support machine learning methods and cross-fitting, or provide estimates on multiplicative scales. To address these needs, we developed the AIPW package implementing the augmented inverse probability weighting (AIPW) estimation of average causal effects in R. Key features of the AIPW package includes cross-fitting and flexible covariate adjustment for observational studies and randomized trials (RCTs). In this paper, we use a simulated RCT to present the implementation of the AIPW estimator. We also perform a simulation study to evaluate the performance of the AIPW package compared with other doubly robust implementations including CausalGAM, npcausal, tmle, and tmle3. Our simulation shows that the xtbfAIPW package yielded comparable performance to other programs. Furthermore, we also found that cross-fitting substantively decreases the bias and improves the confidence interval coverage for doubly robust estimators fit with machine learning algorithms. Our findings suggest that the AIPW package can be a useful tool for estimating average causal effects with machine learning methods in RCTs and observational studies.

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Estimation of density functionals via cross‐validation

Chacón,

Tenreiro

2024

Statistica Neerlandica

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In density estimation, the mean integrated squared error (MISE) is commonly used as a measure of performance. In that setting, the cross‐validation criterion provides an unbiased estimator of the MISE minus the integral of the squared density. Since the minimum MISE is known to converge to zero, this suggests that the minimum value of the cross‐validation criterion could be regarded as an estimator of minus the integrated squared density. This novel proposal presents the outstanding feature that, unlike all other existing estimators, it does not need the choice of any tuning parameter. Indeed, it is proved here that this approach results in a consistent and efficient estimator, with remarkable performance in practice. Moreover, apart from this base case, it is shown how several other problems on density functional estimation can be similarly handled using this new principle, thus demonstrating full potential for further applications.

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Discussion of “On Nearly Assumption-Free Tests of Nominal Confidence Interval Coverage for Causal Parameters Estimated by Machine Learning”

Cited by 4 publications

References 11 publications

Assumption-lean falsification tests of rate double-robustness of double-machine-learning estimators

Assumption-lean falsification tests of rate double-robustness of double-machine-learning estimators

AIPW: An R Package for Augmented Inverse Probability–Weighted Estimation of Average Causal Effects

Estimation of density functionals via cross‐validation

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