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General Control Functions for Causal Effect Estimation from Instrumental Variables.

Aahlad Puli1, Rajesh Ranganath2

  • 1Computer Science, New York University.

Advances in Neural Information Processing Systems
|May 6, 2021
PubMed
Summary
This summary is machine-generated.

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This study introduces the General Control Function Method (GCFN) for causal effect estimation using instrumental variables (IVs). GCFN effectively constructs control functions to isolate treatment effects, even with complex data structures.

Area of Science:

  • Econometrics
  • Causal Inference
  • Statistical Modeling

Background:

  • Estimating causal effects requires disentangling treatment impact from confounding factors.
  • Instrumental variables (IVs) are crucial external sources of randomness influencing treatment.
  • Control functions, derived from treatment and IVs, aid in effect estimation.

Purpose of the Study:

  • To characterize general control functions for robust causal effect estimation.
  • To develop a novel method, the General Control Function Method (GCFN), for constructing and utilizing control functions.
  • To introduce a semi-supervised approach for control function construction without structural assumptions.

Main Methods:

  • Developed the General Control Function Method (GCFN) for causal effect estimation.

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  • Introduced Variational Decoupling (VDE) as the first stage of GCFN to construct control functions.
  • Proposed a semi-supervised GCFN utilizing observed IVs and confounders for supervision.

    • Main Results:

    • Established a meta-identification result for general control functions in effect estimation.
    • Demonstrated that structural assumptions on the treatment process enable control function construction and guarantee identification.
    • GCFN successfully estimated causal effects on simulated data and historical data on slave export's impact on trust.

    Conclusions:

    • GCFN provides a powerful framework for causal effect estimation using instrumental variables.
    • The semi-supervised GCFN offers flexibility by relaxing assumptions on the treatment process.
    • The method has broad applicability in econometrics and social sciences for uncovering causal relationships.