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Introduction
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Prediction Policy Problems
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Classification:Logistic
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TreeModels:RandomForests
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Fair ML/Data Ethics
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Neural Networks
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Policy Challenge
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<div id="prediction-policy-problems-linear-models-lasso-regression"
class="section level2 tabset tabset-fade tabset-pills">
<h2 class="tabset tabset-fade tabset-pills"><strong>Prediction Policy
Problems: Linear Models & Lasso Regression</strong></h2>
<p><strong>Introducing the Prediction Policy Framework</strong></p>
<p>In the video-lecture below you’ll be given a brief introduction to
the prediction policy framework, and a primer on machine learning.
Please take a moment to watch the 20 minute video.</p>
<center>
<div class="vembedr">
<div>
<iframe src="https://www.youtube.com/embed/sFbKe4O0tEQ" width="533" height="300" frameborder="0" allowfullscreen="" data-external="1"></iframe>
</div>
</div>
</center>
<p><br> Are you still wondering what the difference is between Machine
Learning and Econometrics? Take a few minutes to watch the video below.
<br></p>
<center>
<div class="vembedr">
<div>
<iframe src="https://www.youtube.com/embed/gx_MBxMu0m4" width="533" height="300" frameborder="0" allowfullscreen="" data-external="1"></iframe>
</div>
</div>
</center>
<p><br></p>
<p>After watching the videos, we have a practical exercise.</p>
<p><strong>Predicting social assistance beneficiaries</strong></p>
<p>A key problem in the design of Social Policies is the identification
of people in need of social assistance. Social policies usually work
with tight budgets and limited fiscal space. To allocate resources
efficiently, benefits need to be targeted to those who need them most.
Yet, identifying needs isn’t easy and misclassifications can have severe
and irreversible effects on people in need.</p>
<p>Think of a social protection programme that allocates food vouchers
to families with children at risk of malnutrition, or a programme that
establishes needs-based school grants. What happens when these limited
and finite resources are given to people that could do without, and
those who need them most are excluded from them?</p>
<p>In this block we’ll work with real-world data from the country of
Malawi to predict cash-transfer programme beneficiaries: People who live
in poverty and need government assistance to make ends meet. The data
comes from McBride and Nichol’s (2018) paper <a
href="https://elibrary.worldbank.org/doi/abs/10.1093/wber/lhw056">Retooling
poverty targeting using out-of-sample validation and machine
learning</a>.</p>
<p><strong>Discussion Points</strong></p>
<p>The points below are meant to help you think critically about why
we’re about to embark on a machine learning - targeting exercise.
<br></p>
<ul>
<li>
Why is this a prediction policy problem? What would be a causal
inference problem in this setting? Is it a regression or a
classification problem?
</li>
<li>
Which variables and characteristics that we include in the prediction
model can make a big difference?
<ul>
<li>
Programmatically and conceptually, which type of characteristics do we
want to consider for the prediction model?
</li>
<li>
Technically, how do we select which variables to include in a prediction
model? How is this different from a causal inference problem?
</li>
</ul>
</li>
<li>
What are the practical implications of the bias-variance tradeoff in
this application?
</li>
<li>
What are potential risks of such a data driven targeting approach?
</li>
</ul>
<p><br></p>
<p><strong>If you’d like to learn more about Social Protection Policies,
take a look at this video Alex has made for us with a brilliant summary
of the field</strong></p>
<p>(Yes, more videos!)</p>
<center>
<div class="vembedr">
<div>
<iframe src="https://www.youtube.com/embed/Pn9DntDejKU" width="533" height="300" frameborder="0" allowfullscreen="" data-external="1"></iframe>
</div>
</div>
</center>
<div id="r-practical" class="section level3">
<h3><strong>R practical</strong></h3>
<p>You can download the dataset by clicking on the button below.
<br></p>
<a href="https://github.com/michelleg06/Machine-Learning-for-Public-Policy/blob/main/malawi.csv">
<button class="btn btn-info"><i class="fa fa-save"></i> Download malawi df (.csv)</button>
</a>
<p>The script below is a step by step on how to go about coding a
predictive model using a <em>linear regression</em>. Despite its
simplicity and transparency, i.e. the ease with which we can interpret
its results, a linear model is not without challenges in machine
learning.</p>
<h3>
<ol style="list-style-type: decimal">
<li>Preliminaries: working directory, libraries, data upload
</h3>
<br></li>
</ol>
<pre class="r"><code>rm(list = ls()) # this line cleans your Global Environment.
setwd("/Users/michellegonzalez/Documents/GitHub/Machine-Learning-for-Public-Policy") # set your working directory
# Libraries
cat("
If this is your first time using R, you need to install the libraries before loading them.
To do that, you can uncomment the line that starts with install.packages(...) by removing the # symbol.
")</code></pre>
<pre><code>##
## If this is your first time using R, you need to install the libraries before loading them.
## To do that, you can uncomment the line that starts with install.packages(...) by removing the # symbol.
## </code></pre>
<pre class="r"><code>#install.packages("dplyr", "tidyverse", "caret", "corrplot", "Hmisc", "modelsummary", "plyr", "gt", "stargazer", elasticnet")
library(dplyr) # core package for dataframe manipulation. Usually installed and loaded with the tidyverse, but sometimes needs to be loaded in conjunction to avoid warnings.
library(tidyverse) # a large collection of packages for data manipulation and visualisation.
library(caret) # a package with key functions that streamline the process for predictive modelling
library(corrplot) # a package to plot correlation matrices
library(Hmisc) # a package for general-purpose data analysis
library(modelsummary) # a package to describe model outputs
library(skimr) # a package to describe dataframes
library(plyr) # a package for data wrangling
library(gt) # a package to edit modelsummary (and other) tables
library(stargazer) # a package to visualise model output
data_malawi <- read_csv("malawi.csv") # the file is directly read from the working directory/folder previously set</code></pre>
<h3>
<ol start="2" style="list-style-type: decimal">
<li>Get to know your data: visualisation and pre-processing
</h3>
<br></li>
</ol>
<pre class="r"><code>skim(data_malawi) # describes the dataset in a nice format </code></pre>
<table style="width: auto;" class="table table-condensed">
<caption>
Data summary
</caption>
<tbody>
<tr>
<td style="text-align:left;">
Name
</td>
<td style="text-align:left;">
data_malawi
</td>
</tr>
<tr>
<td style="text-align:left;">
Number of rows
</td>
<td style="text-align:left;">
11280
</td>
</tr>
<tr>
<td style="text-align:left;">
Number of columns
</td>
<td style="text-align:left;">
38
</td>
</tr>
<tr>
<td style="text-align:left;">
_______________________
</td>
<td style="text-align:left;">
</td>
</tr>
<tr>
<td style="text-align:left;">
Column type frequency:
</td>
<td style="text-align:left;">
</td>
</tr>
<tr>
<td style="text-align:left;">
character
</td>
<td style="text-align:left;">
2
</td>
</tr>
<tr>
<td style="text-align:left;">
numeric
</td>
<td style="text-align:left;">
36
</td>
</tr>
<tr>
<td style="text-align:left;">
________________________
</td>
<td style="text-align:left;">
</td>
</tr>
<tr>
<td style="text-align:left;">
Group variables
</td>
<td style="text-align:left;">
None
</td>
</tr>
</tbody>
</table>
<p><strong>Variable type: character</strong></p>
<table>
<thead>
<tr>
<th style="text-align:left;">
skim_variable
</th>
<th style="text-align:right;">
n_missing
</th>
<th style="text-align:right;">
complete_rate
</th>
<th style="text-align:right;">
min
</th>
<th style="text-align:right;">
max
</th>
<th style="text-align:right;">
empty
</th>
<th style="text-align:right;">
n_unique
</th>
<th style="text-align:right;">
whitespace
</th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:left;">
region
</td>
<td style="text-align:right;">
0
</td>
<td style="text-align:right;">
1
</td>
<td style="text-align:right;">
5
</td>
<td style="text-align:right;">
6
</td>
<td style="text-align:right;">
0
</td>
<td style="text-align:right;">
3
</td>
<td style="text-align:right;">
0
</td>
</tr>
<tr>
<td style="text-align:left;">
eatype
</td>
<td style="text-align:right;">
0
</td>
<td style="text-align:right;">
1
</td>
<td style="text-align:right;">
5
</td>
<td style="text-align:right;">
17
</td>
<td style="text-align:right;">
0
</td>
<td style="text-align:right;">
5
</td>
<td style="text-align:right;">
0
</td>
</tr>
</tbody>
</table>
<p><strong>Variable type: numeric</strong></p>
<table>
<thead>
<tr>
<th style="text-align:left;">
skim_variable
</th>
<th style="text-align:right;">
n_missing
</th>
<th style="text-align:right;">
complete_rate
</th>
<th style="text-align:right;">
mean
</th>
<th style="text-align:right;">
sd
</th>
<th style="text-align:right;">
p0
</th>
<th style="text-align:right;">
p25
</th>
<th style="text-align:right;">
p50
</th>
<th style="text-align:right;">
p75
</th>
<th style="text-align:right;">
p100
</th>
<th style="text-align:left;">
hist
</th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:left;">
lnexp_pc_month
</td>
<td style="text-align:right;">
0
</td>
<td style="text-align:right;">
1
</td>
<td style="text-align:right;">
7.360000e+00
</td>
<td style="text-align:right;">
6.800000e-01
</td>
<td style="text-align:right;">
4.7800e+00
</td>
<td style="text-align:right;">
6.890000e+00
</td>
<td style="text-align:right;">
7.310000e+00
</td>
<td style="text-align:right;">
7.760000e+00
</td>
<td style="text-align:right;">
1.106000e+01
</td>
<td style="text-align:left;">
▁▇▇▁▁
</td>
</tr>
<tr>
<td style="text-align:left;">
hhsize
</td>
<td style="text-align:right;">
0
</td>
<td style="text-align:right;">
1
</td>
<td style="text-align:right;">
4.550000e+00
</td>
<td style="text-align:right;">
2.340000e+00
</td>
<td style="text-align:right;">
1.0000e+00
</td>
<td style="text-align:right;">
3.000000e+00
</td>
<td style="text-align:right;">
4.000000e+00
</td>
<td style="text-align:right;">
6.000000e+00
</td>
<td style="text-align:right;">
2.700000e+01
</td>
<td style="text-align:left;">
▇▂▁▁▁
</td>
</tr>
<tr>
<td style="text-align:left;">
hhsize2
</td>
<td style="text-align:right;">
0
</td>
<td style="text-align:right;">
1
</td>
<td style="text-align:right;">
2.613000e+01
</td>
<td style="text-align:right;">
2.799000e+01
</td>
<td style="text-align:right;">
1.0000e+00
</td>
<td style="text-align:right;">
9.000000e+00
</td>
<td style="text-align:right;">
1.600000e+01
</td>
<td style="text-align:right;">
3.600000e+01
</td>
<td style="text-align:right;">
7.290000e+02
</td>
<td style="text-align:left;">
▇▁▁▁▁
</td>
</tr>
<tr>
<td style="text-align:left;">
agehead
</td>
<td style="text-align:right;">
0
</td>
<td style="text-align:right;">
1
</td>
<td style="text-align:right;">
4.246000e+01
</td>
<td style="text-align:right;">
1.636000e+01
</td>
<td style="text-align:right;">
1.0000e+01
</td>
<td style="text-align:right;">
2.900000e+01
</td>
<td style="text-align:right;">
3.900000e+01
</td>
<td style="text-align:right;">
5.400000e+01
</td>
<td style="text-align:right;">
1.040000e+02
</td>
<td style="text-align:left;">
▅▇▅▂▁
</td>
</tr>
<tr>
<td style="text-align:left;">
agehead2
</td>
<td style="text-align:right;">
0
</td>
<td style="text-align:right;">
1
</td>
<td style="text-align:right;">
2.070610e+03
</td>
<td style="text-align:right;">
1.618600e+03
</td>
<td style="text-align:right;">
1.0000e+02
</td>
<td style="text-align:right;">
8.410000e+02
</td>
<td style="text-align:right;">
1.521000e+03
</td>
<td style="text-align:right;">
2.916000e+03
</td>
<td style="text-align:right;">
1.081600e+04
</td>
<td style="text-align:left;">
▇▃▁▁▁
</td>
</tr>
<tr>
<td style="text-align:left;">
north
</td>
<td style="text-align:right;">
0
</td>
<td style="text-align:right;">
1
</td>
<td style="text-align:right;">
1.500000e-01
</td>
<td style="text-align:right;">
3.600000e-01
</td>
<td style="text-align:right;">
0.0000e+00
</td>
<td style="text-align:right;">
0.000000e+00
</td>
<td style="text-align:right;">
0.000000e+00
</td>
<td style="text-align:right;">
0.000000e+00
</td>
<td style="text-align:right;">
1.000000e+00
</td>
<td style="text-align:left;">
▇▁▁▁▂
</td>
</tr>
<tr>
<td style="text-align:left;">
central
</td>
<td style="text-align:right;">
0
</td>
<td style="text-align:right;">
1
</td>
<td style="text-align:right;">
3.800000e-01
</td>
<td style="text-align:right;">
4.900000e-01
</td>
<td style="text-align:right;">
0.0000e+00
</td>
<td style="text-align:right;">
0.000000e+00
</td>
<td style="text-align:right;">
0.000000e+00
</td>
<td style="text-align:right;">
1.000000e+00
</td>
<td style="text-align:right;">
1.000000e+00
</td>
<td style="text-align:left;">
▇▁▁▁▅
</td>
</tr>
<tr>
<td style="text-align:left;">
rural
</td>
<td style="text-align:right;">
0
</td>
<td style="text-align:right;">
1
</td>
<td style="text-align:right;">
8.700000e-01
</td>
<td style="text-align:right;">
3.300000e-01
</td>
<td style="text-align:right;">
0.0000e+00
</td>
<td style="text-align:right;">
1.000000e+00
</td>
<td style="text-align:right;">
1.000000e+00
</td>
<td style="text-align:right;">
1.000000e+00
</td>
<td style="text-align:right;">
1.000000e+00
</td>
<td style="text-align:left;">
▁▁▁▁▇
</td>
</tr>
<tr>
<td style="text-align:left;">
nevermarried
</td>
<td style="text-align:right;">
0
</td>
<td style="text-align:right;">
1
</td>
<td style="text-align:right;">
3.000000e-02
</td>
<td style="text-align:right;">
1.700000e-01
</td>
<td style="text-align:right;">
0.0000e+00
</td>
<td style="text-align:right;">
0.000000e+00
</td>
<td style="text-align:right;">
0.000000e+00
</td>
<td style="text-align:right;">
0.000000e+00
</td>
<td style="text-align:right;">
1.000000e+00
</td>
<td style="text-align:left;">
▇▁▁▁▁
</td>
</tr>
<tr>
<td style="text-align:left;">
sharenoedu
</td>
<td style="text-align:right;">
0
</td>
<td style="text-align:right;">
1
</td>
<td style="text-align:right;">