• Corpus ID: 202775606

An introduction to flexible methods for policy evaluation

@article{Huber2019AnIT,
  title={An introduction to flexible methods for policy evaluation},
  author={Martin Huber},
  journal={arXiv: Econometrics},
  year={2019}
}
  • M. Huber
  • Published 1 August 2019
  • Economics
  • arXiv: Econometrics
This chapter covers different approaches to policy evaluation for assessing the causal effect of a treatment or intervention on an outcome of interest. As an introduction to causal inference, the discussion starts with the experimental evaluation of a randomized treatment. It then reviews evaluation methods based on selection on observables (assuming a quasi-random treatment given observed covariates), instrumental variables (inducing a quasi-random shift in the treatment), difference-in… 
View PDF on arXiv
6 Citations
Highly Influential Citations
1
Background Citations
2
Methods Citations
3

6 Citations

A Review of Off-Policy Evaluation in Reinforcement Learning

This paper provides a discussion on the efficiency bound of OPE, some of the existing state-of-the-art OPE methods, their statistical properties and some other related research directions that are currently actively explored.

Do soda taxes affect the consumption and health of school-aged children? Evidence from France and Hungary

This paper examines the effect of soda taxes on the consumption behaviour and health of school-aged children in Europe: Hungary imposed a "Public Health Product Tax" on several unhealthy products…

Patenting government funded innovations as a strategy to increase budget for academic vaccine R&D

Vaccine development is a lengthy, expensive and risky venture, with the research and development (R&D) process costing billions of dollars. The pre-clinical stage of vaccine R&D is largely performed…

Impact of Improved Maize Technology Package on Maize Yield Growth of Major Maize Growing Regions of Ethiopia

  • Fitsum Daniel
  • Geography
    Innovative Systems Design and Engineering
  • 2021
This study examines the differential impact of adoption of improved maize technology package (including improved maize varieties, fertilizer of any kind as well as row planting) on maize yield growth…

DIFFERENCE IN IMPACT OF ORGANIC AND/OR INORGANIC FERTILIZER APPLICATION WITH ROW PLANTING ON MAIZE YIELD GROWTH AMONG MAJOR MAIZE GROWING AGRO-ECOLOGICAL ZONES OF ETHIOPIA

This study examines the difference in impact of adoption of fertilizer of any kind (organic, inorganic or both) with row planting on maize yield growth using 800 sample farm households in three major…

Assessing the Short-term Effect of Exchange Rate Liberalisation on Food Import Prices: The Regression Discontinuity in Time Employed for Russian Food Markets in 2014

  • D. Loginova
  • Economics
    Research on World Agricultural Economy
  • 2022
This study summarises the main agricultural policies in Russia during 2014 and uses a sharp regression discontinuity design over time and data from the International Trade Centre to estimate the…

References

SHOWING 1-10 OF 184 REFERENCES

Regression and Weighting Methods for Causal Inference Using Instrumental Variables

Please see the supplementary material for a correction and a new result. Recent researches in econometrics and statistics have gained considerable insights into the use of instrumental variables…

Semiparametric Difference-in-Differences Estimators

The difference-in-differences (DID) estimator is one of the most popular tools for applied research in economics to evaluate the effects of public interventions and other treatments of interest on…

Local Average and Quantile Treatment Effects Under Endogeneity: A Review

Abstract This paper provides a review of methodological advancements in the evaluation of heterogeneous treatment effect models based on instrumental variable (IV) methods. We focus on models that…

Semiparametric instrumental variable estimation of treatment response models

The Role of the Propensity Score in Estimating Dose-Response Functions

Estimation of average treatment effects in observational, or non-experimental in pre-treatment variables. If the number of pre-treatment variables is large, and their distribution varies…

Recursive partitioning for heterogeneous causal effects

This paper provides a data-driven approach to partition the data into subpopulations that differ in the magnitude of their treatment effects, and proposes an “honest” approach to estimation, whereby one sample is used to construct the partition and another to estimate treatment effects for each subpopulation.

Causal Inference With General Treatment Regimes

In this article we develop the theoretical properties of the propensity function, which is a generalization of the propensity score of Rosenbaum and Rubin. Methods based on the propensity score have…

Program evaluation and causal inference with high-dimensional data

This paper shows that a key ingredient enabling honest inference is the use of orthogonal or doubly robust moment conditions in estimating certain reduced form functional parameters, and provides results on honest inference for (function-valued) parameters within this general framework where any high-quality, modern machine learning methods can be used to learn the nonparametric/high-dimensional components of the model.

Identifying the Effect of Changing the Policy Threshold in Regression Discontinuity Models

Abstract Regression discontinuity models are commonly used to nonparametrically identify and estimate a local average treatment effect (LATE).We show that the derivative of the treatment effect with…

Inference on Counterfactual Distributions

In this paper we develop procedures for performing inference in regression models about how potential policy interventions affect the entire marginal distribution of an outcome of interest. These…
...