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Quantifying the share of total apparent effect that is genuinely due to a treatment: A comparison of methods

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  • van Herick, David
  • Mokhtarian, Patricia L.

Abstract

Estimating true treatment effects in the presence of selection bias is a common problem in program evaluation, but it is uncommon to quantify the share of a total apparent effect that is “true”. We identify multiple methods for doing so, in the context of isolating the true causal influence of the built environment on travel behavior in the presence of residential self-selection (RSS). Among the different approaches for dealing with self-selection, we use three: statistical control (SC) modeling, propensity score-based techniques (PS), and sample selection (SS) modeling. Our objective is to identify multiple ways to estimate the proportion of the total apparent effect of the built environment on travel behavior that is due to the built environment itself, which we call the “built environment proportion”, or BEP. We present and evaluate three categories of methods for estimating this measure (each native to one of the approaches): variance explained, modular effects, and treatment effects. A BEP formula associated with a given method can be applied to its native approach or cross-applied to other approaches. We identify and enumerate 47 potential BEPs. The methods presented here can be applied in other contexts involving treatment evaluation in the presence of selection bias.

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  • van Herick, David & Mokhtarian, Patricia L., 2021. "Quantifying the share of total apparent effect that is genuinely due to a treatment: A comparison of methods," Research in Transportation Economics, Elsevier, vol. 90(C).
    • Handle: RePEc:eee:retrec:v:90:y:2021:i:c:s0739885920302183
    DOI: 10.1016/j.retrec.2020.101020
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    Cited by:

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    3. Sung Hoo Kim & Patricia L. Mokhtarian, 2024. "A note on the sample selection (switching regression) model and treatment effects for a log-transformed outcome variable, in the context of residential self-selection," Transportation, Springer, vol. 51(5), pages 1723-1757, October.

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    More about this item

    Keywords

    Program evaluation; Propensity score; Residential self-selection; Sample selection; Statistical control; Treatment effects;
    All these keywords.

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C34 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Truncated and Censored Models; Switching Regression Models
    • C35 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions
    • D04 - Microeconomics - - General - - - Microeconomic Policy: Formulation; Implementation; Evaluation
    • R21 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Household Analysis - - - Housing Demand
    • R41 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - Transportation: Demand, Supply, and Congestion; Travel Time; Safety and Accidents; Transportation Noise

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