Localized Debiased Machine Learning: Efficient Inference on Quantile Treatment Effects and Beyond
Authors: Nathan Kallus, Xiaojie Mao, Masatoshi Uehara
JMLR 2024 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We first study the behavior of LDML in a simulation study. We then demonstrate its use in estimating the QTE of 401(k) eligibility on net financial assets, and the LQTE of 401(k) participation using eligibility as IV. |
| Researcher Affiliation | Academia | Nathan Kallus EMAIL Cornell Tech Cornell University 2 West Loop Rd, NY 10044, USA; Xiaojie Mao EMAIL School of Economics and Management Tsinghua University Beijing, 100084, China; Masatoshi Uehara EMAIL Cornell Tech Cornell University 2 West Loop Rd, NY 10044, USA |
| Pseudocode | No | The paper describes the 'LDML Meta-Algorithm' in Section 2.2 and 'Definition 1 (3-way-cross-fold nuisance estimation)' as structured steps for its methodology. However, these are presented as definitions and descriptions rather than explicitly labeled 'Pseudocode' or 'Algorithm' blocks with formal code-like formatting. |
| Open Source Code | Yes | Replication code is available at https: //github.com/Causal ML/Localized Debiased Machine Learning. |
| Open Datasets | Yes | We use data from Chernozhukov and Hansen (2004) to estimate the QTEs of 401(k) retirement plan eligibility on net financial assets (N = 9915). |
| Dataset Splits | Yes | Randomly permute the data indices and let Dk = { (k 1)N/K +1, . . . , k N/K }, k = 1, . . . , K be a random even K-fold split of the data. |
| Hardware Specification | No | The paper mentions using R packages for boosting, LASSO, and neural networks but does not provide specific hardware details (e.g., GPU/CPU models, processor types, or memory amounts) used for running the experiments. |
| Software Dependencies | No | We consider estimating both propensity score η 2 and conditional cumulative distribution η 1 with each of: boosting (using R package gbm), LASSO (using R package hdm), and a one-hidden-layer neural network (using R package nnet). |
| Experiment Setup | Yes | We consider estimating θ 1 using five different methods. First, we consider LDML applied to the efficient estimating equation (Eq. (3)) with K = 5, K = 2, ˆθ(k) 1,init estimated using 2-fold cross-fitted IPW with random-forest-estimated propensities... In each instantiation of LDML, we construct folds so to ensure a balanced distribution of treated and untreated units, we let K = (K 1)/2, we use the IPW initial estimator for ˆθ1,init, we normalize propensity weights to have mean 1 within each treatment group, we use estimates given by solving the grand-average estimating equation as in Definition 2, and for variance estimation we estimate J using IPW kernel density estimation as in Remark 4. The solution to the LDML-estimated empirical estimating equation must occur at an observed outcome Yi and that we can find the solution using binary search after sorting the data along outcomes. |