pymalts2 0.0.2
Causal Inference Matching Package.
Requires Python: >=3.6

# Introduction

PyMALTS is a learning-to-matching interpretable causal inference method. PyMALTS implements MALTS algorithm proposed by Harsh Parikh, Cynthia Rudin and Alexander Volfovsky in their 2019 paper titled "MALTS: Matching After Learning to Stretch"

## Dependencies

PyMALTS is a Python3 library and it requires Numpy, Pandas, Scipy, Scikit-Learn, Matplotlib and Seaborn.


```python
import pymalts
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
np.random.seed(0)
sns.set()
```

## Reading Data


```python
df = pd.read_csv('example/example_data.csv',index_col=0)
print(df.shape)
df.head()
```

    (2500, 20)





<div>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>X1</th>
      <th>X2</th>
      <th>X3</th>
      <th>X4</th>
      <th>X5</th>
      <th>X6</th>
      <th>X7</th>
      <th>X8</th>
      <th>X9</th>
      <th>X10</th>
      <th>X11</th>
      <th>X12</th>
      <th>X13</th>
      <th>X14</th>
      <th>X15</th>
      <th>X16</th>
      <th>X17</th>
      <th>X18</th>
      <th>outcome</th>
      <th>treated</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>1355</th>
      <td>1.881335</td>
      <td>1.684164</td>
      <td>0.532332</td>
      <td>2.002254</td>
      <td>1.435032</td>
      <td>1.450196</td>
      <td>1.974763</td>
      <td>1.321659</td>
      <td>0.709443</td>
      <td>-1.141244</td>
      <td>0.883130</td>
      <td>0.956721</td>
      <td>2.498229</td>
      <td>2.251677</td>
      <td>0.375271</td>
      <td>-0.545129</td>
      <td>3.334220</td>
      <td>0.081259</td>
      <td>-15.679894</td>
      <td>0</td>
    </tr>
    <tr>
      <th>1320</th>
      <td>0.666476</td>
      <td>1.263065</td>
      <td>0.657558</td>
      <td>0.498780</td>
      <td>1.096135</td>
      <td>1.002569</td>
      <td>0.881916</td>
      <td>0.740392</td>
      <td>2.780857</td>
      <td>-0.765889</td>
      <td>1.230980</td>
      <td>-1.214324</td>
      <td>-0.040029</td>
      <td>1.554477</td>
      <td>4.235513</td>
      <td>3.596213</td>
      <td>0.959022</td>
      <td>0.513409</td>
      <td>-7.068587</td>
      <td>0</td>
    </tr>
    <tr>
      <th>1233</th>
      <td>-0.193200</td>
      <td>0.961823</td>
      <td>1.652723</td>
      <td>1.117316</td>
      <td>0.590318</td>
      <td>0.566765</td>
      <td>0.775715</td>
      <td>0.938379</td>
      <td>-2.055124</td>
      <td>1.942873</td>
      <td>-0.606074</td>
      <td>3.329552</td>
      <td>-1.822938</td>
      <td>3.240945</td>
      <td>2.106121</td>
      <td>0.857190</td>
      <td>0.577264</td>
      <td>-2.370578</td>
      <td>-5.133200</td>
      <td>0</td>
    </tr>
    <tr>
      <th>706</th>
      <td>1.378660</td>
      <td>1.794625</td>
      <td>0.701158</td>
      <td>1.815518</td>
      <td>1.129920</td>
      <td>1.188477</td>
      <td>0.845063</td>
      <td>1.217270</td>
      <td>5.847379</td>
      <td>0.566517</td>
      <td>-0.045607</td>
      <td>0.736230</td>
      <td>0.941677</td>
      <td>0.835420</td>
      <td>-0.560388</td>
      <td>0.427255</td>
      <td>2.239003</td>
      <td>-0.632832</td>
      <td>39.684984</td>
      <td>1</td>
    </tr>
    <tr>
      <th>438</th>
      <td>0.434297</td>
      <td>0.296656</td>
      <td>0.545785</td>
      <td>0.110366</td>
      <td>0.151758</td>
      <td>-0.257326</td>
      <td>0.601965</td>
      <td>0.499884</td>
      <td>-0.973684</td>
      <td>-0.552586</td>
      <td>-0.778477</td>
      <td>0.936956</td>
      <td>0.831105</td>
      <td>2.060040</td>
      <td>3.153799</td>
      <td>0.027665</td>
      <td>0.376857</td>
      <td>-1.221457</td>
      <td>-2.954324</td>
      <td>0</td>
    </tr>
  </tbody>
</table>
</div>



# Using MALTS

## Distance Metric Learning

Setting up the model for learning the distance metric.
1. Variable name for the outcome variable: 'outcome'.
2. Variable name for the treatment variable: 'treated'
3. Data is assigned to python variable df



```python
m = pymalts.malts_mf( outcome='outcome', treatment='treated', data=df) # running MALTS with default setting
```

## Matched Groups

Matched Group matrix (MG_matrix) is NxN matrix with each row corresponding to each query unit and each column corresponds to matched units. Cell (i,j) in the matrix corresponds to the weight of unit j in the matched group of unit i. The weight corresponds to the numbers of times a unit is included in a matched group across M-folds.


```python
m.MG_matrix
```




<div>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>1355</th>
      <th>1320</th>
      <th>1233</th>
      <th>706</th>
      <th>438</th>
      <th>184</th>
      <th>1108</th>
      <th>1612</th>
      <th>816</th>
      <th>131</th>
      <th>...</th>
      <th>1181</th>
      <th>1698</th>
      <th>916</th>
      <th>59</th>
      <th>2267</th>
      <th>1520</th>
      <th>1408</th>
      <th>909</th>
      <th>603</th>
      <th>2285</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>1355</th>
      <td>4.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>2.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>...</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>3.0</td>
      <td>0.0</td>
      <td>3.0</td>
    </tr>
    <tr>
      <th>1320</th>
      <td>0.0</td>
      <td>4.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>1.0</td>
      <td>4.0</td>
      <td>0.0</td>
      <td>...</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
    </tr>
    <tr>
      <th>1233</th>
      <td>0.0</td>
      <td>0.0</td>
      <td>4.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>...</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
    </tr>
    <tr>
      <th>706</th>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>4.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>1.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>...</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>1.0</td>
      <td>0.0</td>
      <td>0.0</td>
    </tr>
    <tr>
      <th>438</th>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>4.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>...</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>1.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
    </tr>
    <tr>
      <th>...</th>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
    </tr>
    <tr>
      <th>1520</th>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>2.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>...</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>4.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
    </tr>
    <tr>
      <th>1408</th>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>3.0</td>
      <td>0.0</td>
      <td>...</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>4.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
    </tr>
    <tr>
      <th>909</th>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>1.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>...</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>4.0</td>
      <td>0.0</td>
      <td>0.0</td>
    </tr>
    <tr>
      <th>603</th>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>...</td>
      <td>0.0</td>
      <td>2.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>4.0</td>
      <td>0.0</td>
    </tr>
    <tr>
      <th>2285</th>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>...</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>4.0</td>
    </tr>
  </tbody>
</table>
<p>2500 rows Ã— 2500 columns</p>
</div>



Visualizing matched group matrix as heatmap


```python
fig = plt.figure(figsize=(10,10))
sns.heatmap(m.MG_matrix)
```


![png](example/output_15_1.png)


Accessing the matched group for an example unit with index equal to "1" and visualizing the weights as bar-chart


```python
MG1 = m.MG_matrix.loc[1] #matched group for unit "1"
MG1[MG1>1].sort_values(ascending=False).plot(kind='bar',figsize=(20,5)) #Visualizing all the units matched to unit 1 more than once
```


![png](example/output_17_1.png)


## ATE and CATE Estimates


```python
m.CATE_df #each row is a cate estimate for a corresponding unit
```




<div>

<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>avg.CATE</th>
      <th>std.CATE</th>
      <th>outcome</th>
      <th>treated</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>47.232061</td>
      <td>21.808950</td>
      <td>-15.313091</td>
      <td>0.0</td>
    </tr>
    <tr>
      <th>1</th>
      <td>40.600643</td>
      <td>21.958906</td>
      <td>-16.963202</td>
      <td>0.0</td>
    </tr>
    <tr>
      <th>2</th>
      <td>40.877320</td>
      <td>22.204570</td>
      <td>9.527929</td>
      <td>1.0</td>
    </tr>
    <tr>
      <th>3</th>
      <td>37.768578</td>
      <td>19.740320</td>
      <td>-3.940218</td>
      <td>0.0</td>
    </tr>
    <tr>
      <th>4</th>
      <td>39.920257</td>
      <td>21.744433</td>
      <td>-8.011915</td>
      <td>0.0</td>
    </tr>
    <tr>
      <th>...</th>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
    </tr>
    <tr>
      <th>2495</th>
      <td>49.227788</td>
      <td>21.581176</td>
      <td>-14.529871</td>
      <td>0.0</td>
    </tr>
    <tr>
      <th>2496</th>
      <td>42.352355</td>
      <td>21.385861</td>
      <td>19.570055</td>
      <td>1.0</td>
    </tr>
    <tr>
      <th>2497</th>
      <td>43.737763</td>
      <td>19.859275</td>
      <td>-16.342666</td>
      <td>0.0</td>
    </tr>
    <tr>
      <th>2498</th>
      <td>41.189297</td>
      <td>20.346711</td>
      <td>-9.165242</td>
      <td>0.0</td>
    </tr>
    <tr>
      <th>2499</th>
      <td>45.427037</td>
      <td>23.762884</td>
      <td>-17.604829</td>
      <td>0.0</td>
    </tr>
  </tbody>
</table>
<p>2500 rows Ã— 4 columns</p>
</div>



Estimate Average Treatment Effect (ATE)


```python
ATE = m.CATE_df['avg.CATE'].mean()
ATE
```




    42.29673993471417



Visualizing ATE and probability density function of CATE (using KDE plot)


```python
fig = plt.figure(figsize=(10,5))
sns.kdeplot(m.CATE_df['avg.CATE'],shade=True)
plt.axvline(ATE,c='black')
plt.text(ATE-4,0.04,'$\hat{ATE}$',rotation=90)
```




    Text(38.29673993471417, 0.04, '$\\hat{ATE}$')




![png](example/output_23_1.png)


## MALTS Arguments

1. **outcome**: 	Name of the outcome variable column in the data
2. **treatment**: 	Name of the treatment variable column in the data
3. **data**: 			Data in the pandas Dataframe format
4. **discrete**: 		List of column names which are discrete (dummified); Default=[]
5. **C**: 				Regularization constant; Default=1
6. **k_tr**: 			Size of matched group in training step; Default=15
7. **k_est**: 			Size of matched group in estimation step; Default=50
8. **estimator**: 		CATE estimator inside a matched group; Default='linear'; Options: 'linear','mean' or 'RF'
9. **smooth_cate**: 	Boolean to smoothen CATE estimates by fitting a regression model; Default=True
10. **reweight**: 		Reweight treated and control groups as per their respective sample sizes in training step; Default=False,
11. **n_splits**: 		Number of splits of the data for n_split-fold procedure; Default=5
12. **n_repeats**: 		Number of repeats of the whole procedure; Default=1
13. **output_format**: 	Output format of CATE dataframe; Default='brief'; Options: 'brief' or 'full'

# Visualization

## Looking Inside a Matched-Group

Plotting the X1 and X2 marginal of matched-group of unit "0" 


```python
MG0 = m.MG_matrix.loc[0] #fetching the matched group
matched_units_idx = MG0[MG0!=0].index #getting the indices of the matched units 
matched_units = df.loc[matched_units_idx] #fetching the data of matched units

sns.lmplot(x='X1', y='X2', hue='treated', data=matched_units,palette="Set1") #plotting the MG on (X1,X2)
plt.scatter(x=[df.loc[0,'X1']],y=[df.loc[0,'X2']],c='black',s=100) #plotting the unit-0 on (X1,X2)
plt.title('Matched Group for Unit-0') #setting title of the plot
```




    Text(0.5, 1, 'Matched Group for Unit-0')




![png](example/output_29_1.png)


## Plotting CATE versus covariate

Plotting CATE v.s. X1


```python
data_w_cate = df.join(m.CATE_df, rsuffix='_').drop(columns=['outcome_','treated_']) #joining cate dataframe with data

sns.regplot( x='X1', y='avg.CATE', data=data_w_cate, scatter_kws={'alpha':0.5,'s':2}, line_kws={'color':'black'}, order=2 ) #fitting a degree 2 polynomial X1 on CATE
```





![png](example/output_32_1.png)