Nested Nonparametric Instrumental Variable Regression
  • Home
  • Installation & Replication
    • 1. Installation
      • 1.1. Create and activate an environment
      • 1.2. Install dependencies
      • 1.3. Install the package
    • 2. What’s in the box?
    • 3. Quick start (library)
    • 4. Reproducing the simulations
      • 4.1. Folder layout
      • 4.2. Nonparametric simulations (Table 1)
      • 4.3. Semiparametric coverage simulations (Table 2)
      • 4.4. Unified Slurm options
      • 4.5. Notes on parallelism & threads
    • 5. Empirical replications (notebooks)
      • Longitudinal Nested NPIV: Sequential (AGMM) vs Simultaneous (AGMM2L2)
      • Semiparametrics: DML (Mediated) with Neural Nets - AGMM (Sequential) and AGMM2L2 (Simultaneous)
    • 6. Repository structure
    • 7. Citing
    • 8. License
  • Estimators for Sequential and Simultaneous Nested NPIV
    • Overview
    • Assumptions
    • Notation
    • Estimator Objectives
    • Progressive Recipe
    • Estimator Families
      • Regularized Kernel Hilbert Space
        • Closed form - Estimator 1
        • Closed form - Estimator 2
        • Closed form - Estimator 3
        • Closed form - Estimator 3 (RKHS norm)
      • Random Forest
        • Estimator 1
        • Estimator 2
        • Estimator 3
        • Estimator 3 - (Function class bounded)
      • Neural Networks
        • oadam.OAdam
        • Subsetted Estimator
      • Sparse Linear Function Spaces (\(\ell_1-\ell_1\))
        • Estimator 1
        • Estimator 2
        • Estimator 3 - (Ridge)
        • Estimator 3 - (\(\ell_1\)-norm)
      • Regularized Linear Function Spaces (\(\ell_2-\ell_2\))
        • Estimator 1
        • Estimator 2
        • Estimator 3 - (Ridge)
        • Estimator 3 - (\(\ell_2\)-norm)
      • Linear Class
        • tsls.tsls
        • tsls.regtsls
    • Related Pages
  • Estimation Diagnostics
    • Overview
    • Assumptions
    • Notation
    • Progressive Recipe
    • Canonical Diagnostics Reference
      • Universal Diagnostics API
        • Assumptions
        • Notation
        • Diagnostic A: Relative Well-posedness
        • Growing-sieve diagnostic (J and eta paths)
        • Post-estimation error-direction diagnostic (kappa_eff)
        • Post-estimation sieve path (kappa_eff by J and eta)
        • Availability and workflow
        • Plug-and-play usage with dataset blocks
        • Canonical nested NPIV shortcut
    • Related Pages
  • Semiparametric Estimation
    • Overview
    • Assumptions
    • Notation
    • Debiased Machine Learning Meta-Algorithm
    • Progressive Recipe
    • Model-Specific Semiparametric APIs
      • NPIV
        • dml_npiv.DML_npiv
      • Mediation Analysis
        • Different Estimands
      • Long-term Effect Analysis
        • Surrogacy Model
        • Latent Unconfounded Model
    • Related Pages
  • API Documentation
    • Overview
    • Longitudinal Estimator APIs
      • Longitudinal API Overview
        • RKHS Estimators
        • Ensemble and Random Forest Estimators
        • Neural Network Estimators
        • Sparse and Regularized Linear Estimators
        • TSLS Baselines
    • Diagnostics APIs
    • Semiparametric APIs
      • Semiparametric API Overview
        • NPIV Functional Inference
        • Mediation Analysis
        • Long-Term Effect Estimation
    • Related Pages
Nested Nonparametric Instrumental Variable Regression
  • »
  • Overview: module code

All modules for which code is available

  • agmm
  • agmm2
  • dml_longterm
  • dml_mediated
  • dml_npiv
  • ensemble
  • ensemble2
  • nnpiv.diagnostics.core
  • nnpiv.ensemble.ensemble
  • nnpiv.ensemble.ensemble2
  • nnpiv.linear.sparse2_l1_l1
  • nnpiv.linear.sparse2_l2_l2
  • nnpiv.linear.sparse_l1_l1
  • nnpiv.linear.sparse_l2_l2
  • nnpiv.neuralnet.agmm
  • nnpiv.neuralnet.agmm2
  • nnpiv.rkhs.rkhs2iv
  • nnpiv.rkhs.rkhsiv
  • nnpiv.semiparametrics.dml_longterm
  • nnpiv.semiparametrics.dml_mediated
  • nnpiv.semiparametrics.dml_npiv
  • nnpiv.tsls.tsls
  • oadam
  • rkhs2iv
  • rkhsiv
  • sparse2_l1_l1
  • sparse2_l2_l2
  • sparse_l1_l1
  • sparse_l2_l2
  • tsls

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