cluster_experiments

A library to run simulation-based power analysis, including cluster-randomized trial data. Also useful to design and analyse cluster-randomized and switchback experiments.

Examples

Hello world

Hello world of the library, non-clustered version. There is an outcome variable analyzed with a linear regression. The perturbator adds a constant effect to treated units, and the splitter is random.

import numpy as np
import pandas as pd
from cluster_experiments import PowerAnalysis

# Create fake data
N = 1_000
df = pd.DataFrame(
    {
        "target": np.random.normal(0, 1, size=N),
    }
)

config = {
    "analysis": "ols_non_clustered",
    "perturbator": "constant",
    "splitter": "non_clustered",
    "n_simulations": 50,
}
pw = PowerAnalysis.from_dict(config)

# Keep in mind that the average effect is the absolute effect added, this is not relative!
power = pw.power_analysis(df, average_effect=0.1)

# You may also get the power curve by running the power analysis with different average effects
power_line = pw.power_line(df, average_effects=[0, 0.1, 0.2])


# A faster method can be used to run the power analysis, using the approximation of
# the central limit theorem, which is stable with less simulations
from cluster_experiments import NormalPowerAnalysis
npw = NormalPowerAnalysis.from_dict(
    {
        "analysis": "ols_non_clustered",
        "splitter": "non_clustered",
        "n_simulations": 5,
    }
)
power_line_normal = npw.power_line(df, average_effects=[0, 0.1, 0.2])

Switchback

Hello world of this library, clustered version. Since it uses dates as clusters, we consider it a switchback experiment. However, if you want to run a clustered experiment, you can use the same code without the dates.

from datetime import date

import numpy as np
import pandas as pd
from cluster_experiments.power_analysis import PowerAnalysis

# Create fake data
N = 1_000
clusters = [f"Cluster {i}" for i in range(100)]
dates = [f"{date(2022, 1, i):%Y-%m-%d}" for i in range(1, 32)]
df = pd.DataFrame(
    {
        "cluster": np.random.choice(clusters, size=N),
        "target": np.random.normal(0, 1, size=N),
        "date": np.random.choice(dates, size=N),
    }
)

config = {
    "cluster_cols": ["cluster", "date"],
    "analysis": "gee",
    "perturbator": "constant",
    "splitter": "clustered",
    "n_simulations": 50,
}
pw = PowerAnalysis.from_dict(config)

print(df)
# Keep in mind that the average effect is the absolute effect added, this is not relative!
power = pw.power_analysis(df, average_effect=0.1)
print(f"{power = }")

Long example

This is a more comprehensive example of how to use this library. There are simpler ways to run this power analysis above but this shows all the building blocks of the library.

from datetime import date

import numpy as np
import pandas as pd
from cluster_experiments.experiment_analysis import GeeExperimentAnalysis
from cluster_experiments.perturbator import ConstantPerturbator
from cluster_experiments.power_analysis import PowerAnalysis, NormalPowerAnalysis
from cluster_experiments.random_splitter import ClusteredSplitter

# Create fake data
N = 1_000
clusters = [f"Cluster {i}" for i in range(100)]
dates = [f"{date(2022, 1, i):%Y-%m-%d}" for i in range(1, 32)]
df = pd.DataFrame(
    {
        "cluster": np.random.choice(clusters, size=N),
        "target": np.random.normal(0, 1, size=N),
        "date": np.random.choice(dates, size=N),
    }
)

# A switchback experiment is going to be run, prepare the switchback splitter for the analysis
sw = ClusteredSplitter(
    cluster_cols=["cluster", "date"],
)

# We use a constant perturbator to add artificial effect on the treated on the power analysis
perturbator = ConstantPerturbator()

# Use gee to run the analysis
analysis = GeeExperimentAnalysis(
    cluster_cols=["cluster", "date"],
)

# Run the power analysis
pw = PowerAnalysis(
    perturbator=perturbator, splitter=sw, analysis=analysis, n_simulations=50, seed=123
)

# Keep in mind that the average effect is the absolute effect added, this is not relative!
power = pw.power_analysis(df, average_effect=0.1)
print(f"{power = }")

# You can also use normal power analysis, that uses central limit theorem to estimate power, and it should be stable in less simulations
npw = NormalPowerAnalysis(
    splitter=sw, analysis=analysis, n_simulations=50, seed=123
)
power = npw.power_analysis(df, average_effect=0.1)
print(f"{power = }")

Features

The library offers the following classes:

• Regarding power analysis:
  • PowerAnalysis: to run power analysis on any experiment design, using simulation
  • NormalPowerAnalysis: to run power analysis on any experiment design using the central limit theorem for the distribution of the estimator
  • ConstantPerturbator: to artificially perturb treated group with constant perturbations
  • BinaryPerturbator: to artificially perturb treated group for binary outcomes
  • RelativePositivePerturbator: to artificially perturb treated group with relative positive perturbations
  • NormalPerturbator: to artificially perturb treated group with normal distribution perturbations
  • BetaRelativePositivePerturbator: to artificially perturb treated group with relative positive beta distribution perturbations
  • BetaRelativePerturbator: to artificially perturb treated group with relative beta distribution perturbations in a specified support interval
  • SegmentedBetaRelativePerturbator: to artificially perturb treated group with relative beta distribution perturbations in a specified support interval, but using clusters
• Regarding splitting data:
  • ClusteredSplitter: to split data based on clusters
  • BalancedClusteredSplitter: to split data based on clusters in a balanced way
  • NonClusteredSplitter: Regular data splitting, no clusters
  • StratifiedClusteredSplitter: to split based on clusters and strata, balancing the number of clusters in each stratus
  • RepeatedSampler: for backtests where we have access to counterfactuals, does not split the data, just duplicates the data for all groups
  • Switchback splitters (the same can be done with clustered splitters, but there is a convenient way to define switchback splitters using switch frequency):
    • SwitchbackSplitter: to split data based on clusters and dates, for switchback experiments
    • BalancedSwitchbackSplitter: to split data based on clusters and dates, for switchback experiments, balancing treatment and control among all clusters
    • StratifiedSwitchbackSplitter: to split data based on clusters and dates, for switchback experiments, balancing the number of clusters in each stratus
    • Washover for switchback experiments:
      • EmptyWashover: no washover done at all.
      • ConstantWashover: accepts a timedelta parameter and removes the data when we switch from A to B for the timedelta interval.
• Regarding analysis:
  • GeeExperimentAnalysis: to run GEE analysis on the results of a clustered design
  • MLMExperimentAnalysis: to run Mixed Linear Model analysis on the results of a clustered design
  • TTestClusteredAnalysis: to run a t-test on aggregated data for clusters
  • PairedTTestClusteredAnalysis: to run a paired t-test on aggregated data for clusters
  • ClusteredOLSAnalysis: to run OLS analysis on the results of a clustered design
  • OLSAnalysis: to run OLS analysis for non-clustered data
  • TargetAggregation: to add pre-experimental data of the outcome to reduce variance
• Other:
  • PowerConfig: to conviently configure PowerAnalysis class

Installation

You can install this package via pip.

pip install cluster-experiments

It may be safer to install via;

python -m pip install cluster-experiments

Contributing

In case you want to use venv as a virtual environment:

python -m venv venv
source venv/bin/activate

After creating the virtual environment (or not), run:

git clone [email protected]:david26694/cluster-experiments.git
cd cluster-experiments
make install-dev