stochatreat 0.0.2

Randomized block assignment using pandas

Stochatreat

Introduction

This is a Python module to employ block randomization using pandas. Mainly thought with RCTs in mind, it also works for any other scenario in where you would like to randomly allocate treatment within blocks or strata.

Installation

For now the easiest way (for me) to use this is to copy stochatreat.py into wherever you'd like to use it, and then import it using:

pip install stochatreat

Usage

Single cluster:

import numpy as np
import pandas as pd
from stochatreat import stochatreat

# make 1000 households in 5 different neighborhoods.
np.random.seed(42)
df = pd.DataFrame(data={'id': list(range(1000)),
                        'nhood': np.random.randint(1, 6, size=1000)})

# randomly assign treatments by neighborhoods.
treats = stochatreat(data=df,              # your dataframe
                     block_cols='nhood',   # the blocking variable
                     treats=2,             # including control
                     idx_col='id',         # the unique id column
                     random_state=42)
# merge back with original data
df = df.merge(treats, how='left', on='id')

# check for allocations
df.groupby('nhood')['treat'].value_counts().unstack()

# previous code should return this
treat  0.0  1.0
nhood          
1      105  105
2       95   95
3       95   95
4      103  103
5      102  102

Multiple clusters and treatment probabilities:

import numpy as np
import pandas as pd
from stochatreat import stochatreat

# make 1000 households in 5 different neighborhoods, with a dummy indicator
np.random.seed(42)
df = pd.DataFrame(data={'id': list(range(1000)),
                        'nhood': np.random.randint(1, 6, size=1000),
                        'dummy': np.random.randint(0, 2, size=1000)})

# randomly assign treatments by neighborhoods and dummy status.
treats = stochatreat(data=df,
                     block_cols=['nhood', 'dummy'],
                     treats=2,
                     probs=[1/3, 2/3],
                     idx_col='id',
                     random_state=42)
# merge back with original data
df = df.merge(treats, how='left', on='id')

# check for allocations
df.groupby(['nhood', 'dummy'])['treat'].value_counts().unstack()

# previous code should return this
treat        0.0  1.0
nhood dummy          
1     0       38   74
      1       33   65
2     0       35   69
      1       29   57
3     0       30   58
      1       34   68
4     0       36   72
      1       33   65
5     0       34   67
      1       35   68

Acknowledgments

• stochatreat is totally inspired by Alvaro Carril's fantastic Stata package: randtreat, which was published in The Stata Journal :trumpet:.
• David McKenzie's fantastic post (and blog) about running RCTs for the World Bank.
• In Pursuit of Balance: Randomization in Practice in Development Field Experiments. Bruhn, McKenzie, 2009