bootCounterfactuals

Function for computing the bootstrapped counterfactuals in the DiSCo method

internal

The method of synthetic controls is a widely-adopted tool for evaluating causal effects of policy changes in settings with observational data. In many settings where it is applicable, researchers want to identify causal effects of policy changes on a treated unit at an aggregate level while having access to data at a finer granularity. This package implements a simple extension of the synthetic controls estimator, developed in Gunsilius (2023) <doi:10.3982/ECTA18260>, that takes advantage of this additional structure and provides nonparametric estimates of the heterogeneity within the aggregate unit. The idea is to replicate the quantile function associated with the treated unit by a weighted average of quantile functions of the control units. The package contains tools for aggregating and plotting the resulting distributional estimates, as well as for carrying out inference on them.

David Van Dijcke

DiSCos

Distributional Synthetic Controls Estimation

Florian Gunsilius

Siyun He

bootCounterfactuals function

<dl><dt>result_t</dt>
<dd>A list containing the results of the DiSCo_CI_iter function</dd>
<dt>t</dt>
<dd>The current time period</dd>
<dt>mixture</dt>
<dd>Logical, indicating whether to use the mixture of distributions approach instead.
See Section 4.3. in gunsilius2023distributional;textualDiSCos. This approach minimizes the distance between the CDFs
instead of the quantile functions, and is preferred for categorical variables. When working with such variables, one should
also provide a list of support points in the <code>grid.cat</code> parameter. When that is provided, this parameter is automatically set to TRUE. Default is FALSE.</dd>
<dt>grid</dt>
<dd>Grid to recompute the CDF on if <code>mixture</code> option is chosen</dd></dl>

Arguments

bootCounterfactuals — bootCounterfactuals

<dl>

<dt>result_t</dt>
<dd>A list containing the results of the DiSCo_CI_iter function</dd>


<dt>t</dt>
<dd>The current time period</dd>


<dt>mixture</dt>
<dd>Logical, indicating whether to use the mixture of distributions approach instead.
See Section 4.3. in gunsilius2023distributional;textualDiSCos. This approach minimizes the distance between the CDFs
instead of the quantile functions, and is preferred for categorical variables. When working with such variables, one should
also provide a list of support points in the <code>grid.cat</code> parameter. When that is provided, this parameter is automatically set to TRUE. Default is FALSE.</dd>


<dt>grid</dt>
<dd>Grid to recompute the CDF on if <code>mixture</code> option is chosen</dd>

</dl>

bootCounterfactuals: bootCounterfactuals

Description

Usage

Value

Arguments