pysmatch

Propensity Score Matching (PSM) is a statistical technique used to address selection bias in observational studies, particularly in the assessment of treatment effects. It involves calculating the propensity score—the probability of receiving treatment given observed covariates—for each unit in both treatment and control groups. Units are then matched based on these scores, making the groups more comparable on the covariates. This method attempts to mimic the effects of a randomized experiment in non-randomized study designs, aiming to estimate the causal effects of an intervention.

pysmatch is an improved and extended version of pymatch, providing a robust tool for propensity score matching in Python. This package fixes known bugs from the original project and introduces new features such as parallel computing and model selection, enhancing performance and flexibility.

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Features

• Bug Fixes: Addresses known issues from the original pymatch project.
• Parallel Computing: Speeds up computation by utilizing multiple CPU cores.
• Model Selection: Supports both linear (logistic regression) and tree-based models for propensity score estimation.

Installation

pysmatch is available on PyPI and can be installed using pip:

pip install pysmatch

#

The best way to get familiar with the package is to work through an example. The example below leaves out much of the theory behind matching and focuses on the application within pysmatch. If interested, Sekhon gives a nice overview in his Introduction to the Matching package in R.

Example

In this example, we aim to match Lending Club users who fully paid off loans (control group) with those who defaulted (test group), based on observable characteristics. This allows us to analyze the causal effect of defaulting on variables such as user sentiment, controlling for confounding variables.

Table of Contents

​ • Data Preparation

​ • Fitting Propensity Score Models

​ • Predicting Propensity Scores

​ • Tuning the Matching Threshold

​ • Matching the Data

​ • Assessing the Match Quality

​ • Conclusion

​ • Additional Resources

​ • Contributing

​ • License

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Data Preparation

First, we import the necessary libraries and suppress any warnings for cleaner output.

import warnings
warnings.filterwarnings('ignore')

import pandas as pd
import numpy as np
from pysmatch.Matcher import Matcher

%matplotlib inline

Next, we load the dataset and select a subset of columns relevant for our analysis.

Load the dataset (loan.csv) and select a subset of columns.

# Define the file path and the fields to load
path = "loan.csv"
fields = [
    "loan_amnt",
    "funded_amnt",
    "funded_amnt_inv",
    "term",
    "int_rate",
    "installment",
    "grade",
    "sub_grade",
    "loan_status"
]

# Load the data
data = pd.read_csv(path, usecols=fields)

Understanding the Variables

​ • loan_amnt: The listed amount of the loan applied for by the borrower.

​ • funded_amnt: The total amount committed to that loan at that point in time.

​ • funded_amnt_inv: The total amount funded by investors for that loan.

​ • term: The number of payments on the loan in months.

​ • int_rate: The interest rate on the loan.

​ • installment: The monthly payment owed by the borrower.

​ • grade: Lending Club assigned loan grade.

​ • sub_grade: Lending Club assigned loan subgrade.

​ • loan_status: Current status of the loan.

Creating Test and Control Groups

We create two groups:

​ • Test Group: Users who defaulted on their loans.

​ • Control Group: Users who fully paid off their loans.

We then encode the loan_status as a binary treatment indicator.

# Create test group (defaulted loans)
test = data[data.loan_status == "Default"].copy()
test['loan_status'] = 1  # Treatment group indicator

# Create control group (fully paid loans)
control = data[data.loan_status == "Fully Paid"].copy()
control['loan_status'] = 0  # Control group indicator

---

Fitting Propensity Score Models

We initialize the Matcher object, specifying the outcome variable (yvar) and any variables to exclude from the model (e.g., unique identifiers).

# Initialize the Matcher
m = Matcher(test, control, yvar="loan_status", exclude=[])

Output:

Formula:
loan_status ~ loan_amnt + funded_amnt + funded_amnt_inv + term + int_rate + installment + grade + sub_grade
n majority: 207723
n minority: 1219

Addressing Class Imbalance

Our data exhibits significant class imbalance, with the majority group (fully paid loans) greatly outnumbering the minority group (defaulted loans). To address this, we set balance=True when fitting the propensity score models, which tells pysmatch to undersample the majority class to create balanced datasets for model training.

We also specify nmodels=100 to train 100 models on different random samples of the data, ensuring that a broader portion of the majority class is used in model training.

Model Selection and Parallel Computing

With pysmatch, you can choose between linear models (logistic regression) and tree-based models (e.g., decision trees) for propensity score estimation. You can also leverage parallel computing to speed up model fitting by specifying the number of jobs (n_jobs).

# Set random seed for reproducibility
np.random.seed(42)

# Fit propensity score models
m.fit_scores(balance=True, nmodels=100, n_jobs=5, model_type='linear')

Output:

Fitting 100 Models on Balanced Samples...
Average Accuracy: 70.21%

Note: The average accuracy indicates the separability of the classes given the observed features. An accuracy significantly above 50% suggests that matching is appropriate.

Predicting Propensity Scores

After fitting the models, we predict propensity scores for all observations in our dataset.

# Predict propensity scores
m.predict_scores()

We can visualize the distribution of propensity scores for the test and control groups.

# Plot propensity score distributions
m.plot_scores()

Interpretation: The plot shows that the test group (defaulted loans) generally has higher propensity scores than the control group, indicating that the model can distinguish between the two groups based on the observed features.

---

Tuning the Matching Threshold

The matching threshold determines how similar two propensity scores must be to be considered a match. A smaller threshold results in closer matches but may reduce the number of matched pairs.

We use the tune_threshold method to find an appropriate threshold that balances match quality and sample size.

m.tune_threshold(method='random')

Based on the plot, a threshold of 0.0001 retains 100% of the test group. We will use this threshold for matching.

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Matching the Data

We perform the matching using the match method, specifying the matching method, number of matches per observation, and the threshold.

# Perform matching
m.match(method="min", nmatches=1, threshold=0.0001)

Understanding the Matching Parameters

​ • method:

​ • "min": Finds the closest match based on the smallest difference in propensity scores.

​ • "random": Selects a random match within the threshold.

​ • nmatches: Number of matches to find for each observation in the test group.

​ • threshold: Maximum allowed difference in propensity scores between matched pairs.

Handling Multiple Matches

Matching with replacement means a control observation can be matched to multiple test observations. We can assess how frequently control observations are used in the matched dataset.

# Assess record frequency in matches
m.record_frequency()

Output:

   freq  n_records
0     1       2264
1     2         68
2     3         10
3     4          2

To account for this in subsequent analyses, we assign weights to each observation based on their frequency.

# Assign weights to matched data
m.assign_weight_vector()

Examining the Matched Data

# View a sample of the matched data
m.matched_data.sort_values("match_id").head(6)

	record_id	weight	loan_amnt	funded_amnt	funded_amnt_inv	term	int_rate	installment	grade	sub_grade	loan_status	scores	match_id
0	0	1.0	18000.0	18000.0	17975.000000	60 months	17.27	449.97	D	D3	1	0.644783	0
2192	191970	1.0	2275.0	2275.0	2275.000000	36 months	16.55	80.61	D	D2	0	0.644784	0
1488	80665	1.0	18400.0	18400.0	18250.000000	36 months	16.29	649.53	C	C4	0	0.173057	1
1	1	1.0	21250.0	21250.0	21003.604048	60 months	14.27	497.43	C	C2	1	0.173054	1
2	2	1.0	5600.0	5600.0	5600.000000	60 months	15.99	136.16	D	D2	1	0.777273	2
1828	153742	1.0	12000.0	12000.0	12000.000000	60 months	18.24	306.30	D	D5	0	0.777270	2

•   record_id: Unique identifier for each observation.
•   weight: Inverse of the frequency of the control observation in the matched dataset.
•   match_id: Identifier for matched pairs.

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Assessing the Match Quality

After matching, it’s crucial to assess whether the covariates are balanced between the test and control groups.

Categorical Variables

We compare the distribution of categorical variables before and after matching using Chi-Square tests and proportional difference plots.

# Compare categorical variables
categorical_results = m.compare_categorical(return_table=True)

Interpretation: The p-values after matching are all above 0.05, indicating that we cannot reject the null hypothesis that the distributions are independent of the group label. The proportional differences have also decreased significantly.

Continuous Variables

For continuous variables, we use Empirical Cumulative Distribution Functions (ECDFs) and statistical tests like the Kolmogorov-Smirnov test.

# Compare continuous variables
continuous_results = m.compare_continuous(return_table=True)

Interpretation: After matching, the ECDFs of the test and control groups are nearly identical, and the p-values from the statistical tests are above 0.05, indicating good balance.

Results Summary

# Display categorical results
print(categorical_results)

	var	before	after
0	term	0.0	0.433155
1	grade	0.0	0.532530
2	sub_grade	0.0	0.986986

# Display continuous results
print(continuous_results)

	var	ks_before	ks_after	grouped_chisqr_before	grouped_chisqr_after	std_median_diff_before	std_median_diff_after	std_mean_diff_before	std_mean_diff_after
0	loan_amnt	0.0	0.530	0.000	1.000	0.207814	0.067942	0.229215	0.013929
1	funded_amnt	0.0	0.541	0.000	1.000	0.208364	0.067942	0.234735	0.013929
2	funded_amnt_inv	0.0	0.573	0.933	1.000	0.242035	0.067961	0.244418	0.013981
3	int_rate	0.0	0.109	0.000	0.349	0.673904	0.091925	0.670445	0.079891
4	installment	0.0	0.428	0.004	1.000	0.169177	0.042140	0.157699	0.014590

Conclusion

Using pysmatch, we successfully matched users who defaulted on loans with those who fully paid off loans, achieving balance across all covariates. This balanced dataset can now be used for causal inference or further analysis, such as assessing the impact of defaulting on user sentiment.

Note: In real-world applications, achieving perfect balance may not always be possible. In such cases, consider adjusting matching parameters or including additional covariates. You may also control for residual imbalance in subsequent analyses.

Additional Resources

​ • Sekhon, J. S. (2011). Multivariate and propensity score matching software with automated balance optimization: The Matching package for R. Journal of Statistical Software, 42(7), 1-52. Link

​ • Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1), 41-55.

Contributing

We welcome contributions from the community. If you encounter any issues or have suggestions for improvements, please submit an issue or a pull request on GitHub.

How to Contribute

​ 1. Fork the repository.

​ 2. Create a new branch for your feature or bugfix.

​ 3. Commit your changes with clear messages.

​ 4. Submit a pull request to the main repository.

License

pysmatch is licensed under the MIT License.

Disclaimer: The data used in this example is for demonstration purposes only. Ensure that you have the rights and permissions to use any datasets in your analyses.