coupon

Data on daily spending and coupon receipt (selective subsample)
This data set is a selective subsample of the data set "couponsretailer" which was constructed for illustrative purposes.

datasets

Various estimators of causal effects based on inverse probability weighting, doubly robust estimation, and double machine learning. Specifically, the package includes methods for estimating average treatment effects, direct and indirect effects in causal mediation analysis, and dynamic treatment effects. The models refer to studies of Froelich (2007) <doi:10.1016/j.jeconom.2006.06.004>, Huber (2012) <doi:10.3102/1076998611411917>, Huber (2014) <doi:10.1080/07474938.2013.806197>, Huber (2014) <doi:10.1002/jae.2341>, Froelich and Huber (2017) <doi:10.1111/rssb.12232>, Hsu, Huber, Lee, and Lettry (2020) <doi:10.1002/jae.2765>, and others.

Hugo Bodory

causalweight

Estimation Methods for Causal Inference Based on Inverse
Probability Weighting

Martin Huber

Jannis Kueck

coupon function

A data frame with 1293 rows and 9 variables:<dl>
<dt>dailyspending</dt>
<dd>outcome: customer's daily spending at the retailer in a specific period</dd><dt>coupons</dt>
<dd>treatment: 1 = customer received at least one coupon in that period; 0 = customer did not receive any coupon</dd><dt>coupons_preperiod</dt>
<dd>coupon reception in previous period: 1 = customer received at least one coupon; 0 = customer did not receive any coupon</dd><dt>dailyspending_preperiod</dt>
<dd>daily spending at the retailer in previous period</dd><dt>income_bracket</dt>
<dd>income group: 1 = lowest to 12 = highest</dd><dt>age_range</dt>
<dd>age of customer: 1 = 18-25; 2 = 26-35; 3 = 36-45; 4 = 46-55; 5 = 56-70; 6 = 71 plus</dd><dt>married</dt>
<dd>marital status: 1 = married; 0 = unmarried</dd><dt>rented</dt>
<dd>dwelling type: 1 = rented; 0 = owned</dd><dt>family_size</dt>
<dd>number of family members: 1 = 1; 2 = 2; 3 = 3; 4 = 4; 5 = 5 plus</dd>
</dl>

Format

Data on daily spending and coupon receipt (selective subsample)
This data set is a selective subsample of the data set "couponsretailer" which was constructed for illustrative purposes. — coupon

A data frame with 1293 rows and 9 variables:<dl>
<dt>dailyspending</dt>
<dd>outcome: customer's daily spending at the retailer in a specific period</dd>

<dt>coupons</dt>
<dd>treatment: 1 = customer received at least one coupon in that period; 0 = customer did not receive any coupon</dd>

<dt>coupons_preperiod</dt>
<dd>coupon reception in previous period: 1 = customer received at least one coupon; 0 = customer did not receive any coupon</dd>

<dt>dailyspending_preperiod</dt>
<dd>daily spending at the retailer in previous period</dd>

<dt>income_bracket</dt>
<dd>income group: 1 = lowest to 12 = highest</dd>

<dt>age_range</dt>
<dd>age of customer: 1 = 18-25; 2 = 26-35; 3 = 36-45; 4 = 46-55; 5 = 56-70; 6 = 71 plus</dd>

<dt>married</dt>
<dd>marital status: 1 = married; 0 = unmarried</dd>

<dt>rented</dt>
<dd>dwelling type: 1 = rented; 0 = owned</dd>

<dt>family_size</dt>
<dd>number of family members: 1 = 1; 2 = 2; 3 = 3; 4 = 4; 5 = 5 plus</dd>


</dl>

Data on daily spending and coupon receipt (selective subsample)
This data set is a selective subsample of the data set "couponsretailer" which was constructed for illustrative purposes.

coupon: Data on daily spending and coupon receipt (selective subsample) This data set is a selective subsample of the data set "couponsretailer" which was constructed for illustrative purposes.

Description

Usage

Arguments

Format