DAPSm-Analysis

Code to perform data analysis for the DAPSm paper

In general, repeating the data analysis requires that one would need to

• Save all scripts at the same directory
• Save data sets from Dataverse link
• Specify the directory where the scripts and data sets are saved
• Run the analysis script Data_analysis.R.

But first...

Before running the analysis

Have all the pieces!

In order to run the analysis we need two data sets:

1. Data set on power plant characteristics available at https://dataverse.harvard.edu/dataverse/dapsm as "Power Plant Emissions Data".
2. Data set on ozone, temperature, and Census information measured at the ozone AQS monitoring sites, available at https://dataverse.harvard.edu/dataverse/dapsm as "Ozone, Temperature and Census Raw Data".
3. Make sure that the power plant data set is in .csv format, and the monitor information is in .dat format. Do not rename the files.

We further need the following R scripts:

1. config.R: The file including the configuration of the analysis.
2. AverageSulfurContent_function.R: Function that calculates the mean of the reported sulfur content.
3. CaliperEst_function.R: Function that fits matching in distance calipers.
4. CleanPPunits_function.R: Dropping some units that were, for example, not operating during the study period.
5. CreateNOxControlsFunction.R: Function that created the different NOx emission control indicators.
6. Data_analysis_functions.R: Functions that drop columns that we do not want to include in the analysis and reform others.
7. Data_analysis_models_functions.R: Functions that fit the various methods that are fit on the data set. Depend on other scripts.
8. Data_analysis.R: Contains the main data analysis code which loads all data sets and sources in the necessary functions.
9. dist.caliper_function.R': Function that returns a matrix of matched pairs using distance caliper.
10. expit.R: The expit function.
11. GBMPropScores_function.R: Used to estimate the propensity scores using GBM.
12. make_data_functions.R: This file includes functions that load the unit level data, aggregate unit level to facility level, and link to ozone monitoring sites.
13. OptPSmatch_function.R: Function that is used for optimal matching. Similar to PSmatchEst_function.R that uses greedy matching.
14. PSmatchEst_function.R: Function that is used to match data on a set of estimated propensity scores and return estimates of the causal effect, matched pairs, and balance.
15. PredictHeatInput.R: Function that is called to predict heat input at the unit level data.
16. StandDiff_function.R: Function that calculates the standardized difference of means.
17. keele_match_function.R: Function that matches based on the Keele et al (2015) method.
18. 01_subsetmatch2.R, 02_errorhandling.R, 03_problemparameters2.R, 04_constraintmatrix2.R: Functions supplied by Keele et al (2015)

Make sure that the four functions by Keele et al (2015) are in a folder named 'Keele_et_al_functions', or change the corresponing part of the Data_analysis.R part.

Save scripts and data sets

You will need to specify the directory where all the scripts are (save all scripts in the same directory with the names used here), as well as the directory where the power plant data are, and the file path of the linked ozone, temperature, Census data.

Installing necessary R packages

• In order to download, and link AQS data, Christine Choirat has created a R package called arepa and is available through Github.

• For the analysis using DAPSm, you will need the DAPSm R package which is also available through Github.

In order to download Github packages, you should install devtools on R studio by running install.packages('devtools'), and load the corresponding library library(devtools).

Download the arepa and DAPSm packages using

devtools::install_github("czigler/arepa")
devtools::install_github("gpapadog/DAPSm")

Other R packages are also used in the analysis, and are all available on CRAN: data.table, corrplot, MatchIt, Matching, fields, ggplot2, stringr.

Updating the configuration file

This is something that needs to be done, but should not take more than a couple minutes! In the config.R file, one needs to specify:

• wd: Your working directory, and the path where the scripts are saved.
• data_dir: The path to the folder where the power plant data are saved. File name must match the name of the file provided.
• outcome_analysis: The name of the variable which we consider as the outcome of interest. Options are:
  • mean4maxOzone: The average of the fourth maximum ozone concentration.
  • totNOxemissions: Total emissions of power plants.

The remaining variables should not be changed if one wants to replicate the results of the paper.

• caliper: Caliper used in matching.

• cutoff: The cutoff of ASDM below which a variable is considered balanced.

• weights: The weights over which we fit DAPSm in order to investigate the optimal w.

• year: Can only be changed if one has linked ozone, temperature, Census data on a year other than 2004.

• month: Can only be changed if one has linked ozone, temperature, Census data on months other than June, July and August 2004.

• time_use: A list of the years which will be used in order to predict heat input.

• within_km: How many kilometers is the maximum linkage of power plants to ozone monitoring sites.

• matching_algorithm: Options are optimal or greedy. Set to greedy since optimal matching failed to return any matches.

• remove_unmatchables: Only relevant for optimal matching.

For the method of Keele et al.

• n_matches: Number of matched per treated.
• use_controls: Whether specific controls need to be used
• enforce_constraints
• subset_weight: The $\lambda$ tuning parameter.

Running the analysis script

The main data analysis script is named Data_analysis.R.

• Open Data_analysis.R in R/Rstudio
• Specify the file path where the configuration file is saved as config_path
• Run the full script

Reference

Luke Keele, Rocio Titiunik, and Jose Zubizarreta. Enhancing a Geographic Regression Discontinuity Design Through Matching to Estimate the Effect of Ballot Initiatives on Voter Turnout. Journal of Royal Statistical Society A, 2015.