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Randomized block assignment using pandas

Project description

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

pip install stochatreat

Usage

Single cluster:

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

# 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:

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

# 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

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