# Implements several types of propensity-score matching, balance diagnostics for group characteristics, average treatment effect on the treated (ATT) estimates, and bootstraping to estimate standard errors of the estimated ATT. # # **NB: one should proceed with caution using bootstrap standard errors... there's no guarantee it will work. See ["On the Failure of the Bootstrap For Matching Estimators" (Abadie and Imbens, 2008)](http://scholar.harvard.edu/imbens/files/on_the_failure_of_the_bootstrap_for_matching_estimators.pdf).** #

# Propensity scoring methods:

# **One-to-one:** Matches individuals in the treatment group to a single individual in the control group. Variations include whether or not controls are matched with replacement, whether or not a caliper should be used, and the scale of the caliper.
#

# **One-to-many:** Matches individuals in the treatment group to as many individuals in the control group as possible, subject to some criteria. Variations include whether or not controls are matched with replacement, caliper methods as in one-to-one matching, and k nearest neighbor matching.
# In[1]:
import math
import numpy as np
import scipy
from scipy.stats import binom, hypergeom, gaussian_kde
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.linear_model import LogisticRegression
from ModelMatch import binByQuantiles
import statsmodels.api as sm
# # Propensity score estimation
#
# We use a logistic regression of treatment on covariates to estimate the propensity score. This is not the only way to estimate the propensity score, but it is the most common.
# In[2]:
def computePropensityScore(formula, data, verbosity=1):
'''
Compute propensity scores
Inputs:
formula = string of the form 'Treatment~ covariate1 + covariate2 + ...', where these are column names in data
data = matrix-like object with columns corresponding to terms in the formula
verbosity = whether or not to print glm summary
dependencies: LogisticRegression from sklearn.linear_model
statsmodels as sm
'''
####### Using LogisticRegression from sklearn.linear_model
#propensity = LogisticRegression()
#propensity.fit(predictors, groups)
#return propensity.predict_proba(predictors)[:,1]
####### Using sm.GLM
#predictors = sm.add_constant(predictors, prepend=False)
#glm_binom = sm.GLM(groups, predictors, family=sm.families.Binomial())
####### Using sm.formula.glm with formula call
glm_binom = sm.formula.glm(formula = formula, data = data, family = sm.families.Binomial())
res = glm_binom.fit()
if verbosity:
print res.summary()
return res.fittedvalues
# # Matching
#
#
# We implement several variants of matching: one-to-one matching, one-to-many matching, with or without a caliper, and without or without replacement. Variants of the methods are examined in the following paper.
#

# Austin, P. C. (2014), A comparison of 12 algorithms for matching on the propensity score. Statist. Med., 33: 1057–1069. doi: 10.1002/sim.6004
# In[3]:
def Match(groups, propensity, caliper = 0.05, caliper_method = "propensity", replace = False):
'''
Implements greedy one-to-one matching on propensity scores.
Inputs:
groups = Array-like object of treatment assignments. Must be 2 groups
propensity = Array-like object containing propensity scores for each observation. Propensity and groups should be in the same order (matching indices)
caliper = a numeric value, specifies maximum distance (difference in propensity scores or SD of logit propensity)
caliper_method = a string: "propensity" (default) if caliper is a maximum difference in propensity scores,
"logit" if caliper is a maximum SD of logit propensity, or "none" for no caliper
replace = Logical for whether individuals from the larger group should be allowed to match multiple individuals in the smaller group.
(default is False)
Output:
A series containing the individuals in the control group matched to the treatment group.
Note that with caliper matching, not every treated individual may have a match.
'''
# Check inputs
if any(propensity <=0) or any(propensity >=1):
raise ValueError('Propensity scores must be between 0 and 1')
elif not(0<=caliper<1):
if caliper_method == "propensity" and caliper>1:
raise ValueError('Caliper for "propensity" method must be between 0 and 1')
elif caliper<0:
raise ValueError('Caliper cannot be negative')
elif len(groups)!= len(propensity):
raise ValueError('groups and propensity scores must be same dimension')
elif len(groups.unique()) != 2:
raise ValueError('wrong number of groups: expected 2')
# Transform the propensity scores and caliper when caliper_method is "logit" or "none"
if caliper_method == "logit":
propensity = log(propensity/(1-propensity))
caliper = caliper*np.std(propensity)
elif caliper_method == "none":
caliper = 0
# Code groups as 0 and 1
groups = groups == groups.unique()[0]
N = len(groups)
N1 = groups[groups == 1].index; N2 = groups[groups == 0].index
g1, g2 = propensity[groups == 1], propensity[groups == 0]
# Check if treatment groups got flipped - the smaller should correspond to N1/g1
if len(N1) > len(N2):
N1, N2, g1, g2 = N2, N1, g2, g1
# Randomly permute the smaller group to get order for matching
morder = np.random.permutation(N1)
matches = {}
for m in morder:
dist = abs(g1[m] - g2)
if (dist.min() <= caliper) or not caliper:
matches[m] = dist.argmin() # Potential problem: check for ties
if not replace:
g2 = g2.drop(matches[m])
return (matches)
def MatchMany(groups, propensity, method = "caliper", k = 1, caliper = 0.05, caliper_method = "propensity", replace = True):
'''
Implements greedy one-to-many matching on propensity scores.
Inputs:
groups = Array-like object of treatment assignments. Must be 2 groups
propensity = Array-like object containing propensity scores for each observation. Propensity and groups should be in the same order (matching indices)
method = a string: "caliper" (default) to select all matches within a given range, "knn" for k nearest neighbors,
k = an integer (default is 1). If method is "knn", this specifies the k in k nearest neighbors
caliper = a numeric value, specifies maximum distance (difference in propensity scores or SD of logit propensity)
caliper_method = a string: "propensity" (default) if caliper is a maximum difference in propensity scores,
"logit" if caliper is a maximum SD of logit propensity, or "none" for no caliper
replace = Logical for whether individuals from the larger group should be allowed to match multiple individuals in the smaller group.
(default is True)
Output:
A series containing the individuals in the control group matched to the treatment group.
Note that with caliper matching, not every treated individual may have a match within calipers.
In that case we match it to its single nearest neighbor. The alternative is to throw out individuals with no matches, but then we'd no longer be estimating the ATT.
'''
# Check inputs
if any(propensity <=0) or any(propensity >=1):
raise ValueError('Propensity scores must be between 0 and 1')
elif not(0<=caliper<1):
if caliper_method == "propensity" and caliper>1:
raise ValueError('Caliper for "propensity" method must be between 0 and 1')
elif caliper<0:
raise ValueError('Caliper cannot be negative')
elif len(groups)!= len(propensity):
raise ValueError('groups and propensity scores must be same dimension')
elif len(groups.unique()) != 2:
raise ValueError('wrong number of groups: expected 2')
# Transform the propensity scores and caliper when caliper_method is "logit" or "none"
if method == "caliper":
if caliper_method == "logit":
propensity = log(propensity/(1-propensity))
caliper = caliper*np.std(propensity)
elif caliper_method == "none":
caliper = 0
# Code groups as 0 and 1
groups = groups == groups.unique()[0]
N = len(groups)
N1 = groups[groups == 1].index; N2 = groups[groups == 0].index
g1, g2 = propensity[groups == 1], propensity[groups == 0]
# Check if treatment groups got flipped - the smaller should correspond to N1/g1
if len(N1) > len(N2):
N1, N2, g1, g2 = N2, N1, g2, g1
# Randomly permute the smaller group to get order for matching
morder = np.random.permutation(N1)
matches = {}
for m in morder:
dist = abs(g1[m] - g2)
dist.sort()
if method == "knn":
caliper = dist.iloc[k-1]
# PROBLEM: when there are ties in the knn.
# Need to randomly select among the observations tied for the farthest eacceptable distance
keep = np.array(dist[dist<=caliper].index)
if len(keep):
matches[m] = keep
else:
matches[m] = [dist.argmin()]
if not replace:
g2 = g2.drop(matches[m])
return (matches)
def whichMatched(matches, data, many = False, unique = False):
'''
Simple function to convert output of Matches to DataFrame of all matched observations
Inputs:
matches = output of Match
data = DataFrame of covariates
many = Boolean indicating if matching method is one-to-one or one-to-many
unique = Boolean indicating if duplicated individuals (ie controls matched to more than one case) should be removed
'''
tr = matches.keys()
if many:
ctrl = [m for matchset in matches.values() for m in matchset]
else:
ctrl = matches.values()
# need to remove duplicate rows, which may occur in matching with replacement
temp = pd.concat([data.ix[tr], data.ix[ctrl]])
if unique == True:
return temp.groupby(temp.index).first()
else:
return temp
# In[ ]:
def getWeights(matches, groups):
''' computes weights for mean & regression according to how many times a control was matched in one-many matching'''
ctrl = [m for matchset in matches.values() for m in matchset]
weights = groups.copy()
for c in ctrl:
weights[c] += 1
return weights
def whichMatched(matches, data, many = False, unique = False):
'''
Simple function to convert output of Matches to DataFrame of all matched observations
Inputs:
matches = output of Match
data = DataFrame of covariates
many = Boolean indicating if matching method is one-to-one or one-to-many
unique = Boolean indicating if duplicated individuals (ie controls matched to more than one case) should be removed
'''
tr = matches.keys()
if many:
ctrl = [m for matchset in matches.values() for m in matchset]
else:
ctrl = matches.values()
# need to remove duplicate rows, which may occur in matching with replacement
temp = pd.concat([data.ix[tr], data.ix[ctrl]])
if unique == True:
return temp.groupby(temp.index).first()
else:
return temp
# # Estimate average treatment effect on the treated
#
# Using the matches we found above, we can estimate ATT. We use two estimation strategies below: taking the difference in mean outcomes between treated and control groups and OLS regression.
# In[2]:
def averageTreatmentEffect(groups, response, matches):
'''
Computes ATT using difference in means.
The data passed in should already have unmatched individuals and duplicates removed.
Inputs:
groups = Series containing treatment assignment. Must be 2 groups
response = Series containing response measurements. Indices should match those of groups.
matches = output of Match or MatchMany
'''
if len(groups.unique()) != 2:
raise ValueError('wrong number of groups: expected 2')
groups = (groups == groups.unique()[0])
response = response.groupby(response.index).first()
response1 = []; response0 = []
for k in matches.keys():
response1.append(response[k])
response0.append( (response[matches[k]]).mean() ) # Take mean response of controls matched to treated individual k
return np.array(response1).mean() - np.array(response0).mean()
def regressAverageTreatmentEffect(groups, response, covariates, matches=None, verbosity = 0):
'''
Computes ATT by regression.
This works for one-to-one matching. The data passed in should already have unmatched individuals removed.
Weights argument will be added later for one-many matching
Inputs:
groups = Series containing treatment assignment. Must be 2 groups
response = Series containing response measurements. Indices should match those of groups.
covariates = DataFrame containing the covariates to include in the linear regression
matches = optional: if using one-many matching, should be the output of MatchMany.
Use None for one-one matching.
Dependencies: statsmodels.api as sm, pandas as pd
'''
if len(groups.unique()) != 2:
raise ValueError('wrong number of groups: expected 2')
weights = pd.Series(data = np.ones(len(groups)), index = groups.index)
if matches:
ctrl = [m for matchset in matches.values() for m in matchset]
matchcounts = pd.Series(ctrl).value_counts()
for i in matchcounts.index:
weights[i] = matchcounts[i]
if verbosity:
print weights.value_counts(), weights.shape
X = pd.concat([groups, covariates], axis=1)
X = sm.add_constant(X, prepend=False)
linmodel = sm.WLS(response, X, weights = weights).fit()
return linmodel.params[0], linmodel.bse[0]
# In[ ]:
def sampleWithinGroups(groups, data):
'''
To use in bootstrapping functions.
Sample with replacement from each group, return bootstrap sample dataframe
Inputs:
groups = Series containing treatment assignment. Must be 2 groups
data = Dataframe containing observations from which to create bootstrap sample.
'''
bootdata = pd.DataFrame()
for g in groups.unique():
sample = np.random.choice(data.index[data.groups==g], sum(groups == g), replace = True)
newdata =(data[data.groups==g]).ix[sample]
bootdata = bootdata.append(newdata)
bootdata.index = range(len(groups))
return bootdata
def bootstrapATT(groups, response, propensity, many=True, B = 500, method = "caliper", k = 1, caliper = 0.05, caliper_method = "propensity", replace = False):
'''
Computes bootstrap standard error of the average treatment effect on the treated.
Sample observations with replacement, within each treatment group. Then match them and compute ATT.
Repeat B times and take standard deviation.
Inputs:
groups = Series containing treatment assignment. Must be 2 groups
response = Series containing response measurements
propensity = Series containing propensity scores
many = Boolean: are we using one-many matching?
B = number of bootstrap replicates. Default is 500
caliper, caliper_method, replace = arguments to pass to Match or MatchMany
method, k = arguments to pass to MatchMany
'''
if len(groups.unique()) != 2:
raise ValueError('wrong number of groups: expected 2')
data = pd.DataFrame({'groups':groups, 'response':response, 'propensity':propensity})
boot_ate = np.empty(B)
for i in range(B):
bootdata = sampleWithinGroups(groups, data)
if many:
pairs = MatchMany(bootdata.groups, bootdata.propensity, method = method, k = k, caliper = caliper, caliper_method = caliper_method, replace = replace)
else:
pairs = Match(bootdata.groups, bootdata.propensity, caliper = caliper, caliper_method = caliper_method, replace = replace)
boot_ate[i] = averageTreatmentEffect(bootdata.groups, bootdata.response, matches = pairs)
return boot_ate.std()
def bootstrapRegression(groups, response, propensity, covariates, many = True, B = 500, method = "caliper", k = 1, caliper = 0.05, caliper_method = "propensity", replace = False):
'''
Computes bootstrap standard error of the ATT.
Sample observations with replacement, within each treatment group. Then match them and compute ATT.
Repeat B times and take standard deviation.
Inputs:
groups = Series containing treatment assignment. Must be 2 groups
response = Series containing response measurements
propensity = Series containing propensity scores
covariates = DataFrame containing covariates to use in regression
many = Boolean: are we using one-many matching?
B = number of bootstrap replicates. Default is 500
caliper, caliper_method, replace = arguments to pass to Match or MatchMany
method, k = arguments to pass to MatchMany
'''
if len(groups.unique()) != 2:
raise ValueError('wrong number of groups: expected 2')
data = pd.DataFrame({'groups':groups, 'response':response, 'propensity':propensity})
data = pd.concat([data, covariates], axis=1)
boot_ate = np.empty(B)
for i in range(B):
bootdata = sampleWithinGroups(groups, data)
if many:
pairs = MatchMany(bootdata.groups, bootdata.propensity, method = method, k = k, caliper = caliper, caliper_method = caliper_method, replace = replace)
matched = whichMatched(pairs, bootdata, many = True, unique = True)
boot_ate[i] = regressAverageTreatmentEffect(matched.groups, matched.response, matched.ix[:,3:], matches=pairs, verbosity = 0)[0]
else:
pairs = Match(bootdata.groups, bootdata.propensity, caliper = caliper, caliper_method = caliper_method, replace = replace)
matched = whichMatched(pairs, bootdata, many = False)
boot_ate[i] = regressAverageTreatmentEffect(matched.groups, matched.response, matched.ix[:,3:], matches=None, verbosity = 0)[0]
return boot_ate.std()
# # Balance diagnostics
#
# After matching, we hope that the treatment group and matched controls are *close* in distribution with respect to each of their covariates.
# In[1]:
def Balance(groups, covariates):
'''
Computes absolute difference of means and standard error for covariates by group
'''
means = covariates.groupby(groups).mean()
dist = abs(means.diff()).ix[1]
std = covariates.groupby(groups).std()
n = groups.value_counts()
se = std.apply(lambda(s): np.sqrt(s[0]**2/n[0] + s[1]**2/n[1]))
return dist, se
def plotScores(groups, propensity, matches, many=True):
'''
Plot density of propensity scores for each group before and after matching
Inputs: groups = treatment assignment, pre-matching
propensity = propensity scores, pre-matching
matches = output of Match or MatchMany
many = indicator - True if one-many matching was done (default is True), otherwise False
'''
pre = pd.DataFrame({'groups':groups, 'propensity':propensity})
post = whichMatched(matches, pre, many = many, unique = False)
plt.figure(1)
plt.subplot(121)
density0 = scipy.stats.gaussian_kde(pre.propensity[pre.groups==0])
density1 = scipy.stats.gaussian_kde(pre.propensity[pre.groups==1])
xs = np.linspace(0,1,1000)
#density0.covariance_factor = lambda : 0.5
#density0._compute_covariance()
#density1.covariance_factor = lambda : 0.5
#density1._compute_covariance()
plt.plot(xs,density0(xs),color='black')
plt.fill_between(xs,density1(xs),color='gray')
plt.title('Before Matching')
plt.xlabel('Propensity Score')
plt.ylabel('Density')
plt.subplot(122)
density0_post = scipy.stats.gaussian_kde(post.propensity[post.groups==0])
density1_post = scipy.stats.gaussian_kde(post.propensity[post.groups==1])
xs = np.linspace(0,1,1000)
#density0.covariance_factor = lambda : 0.5
#density0._compute_covariance()
#density1.covariance_factor = lambda : 0.5
#density1._compute_covariance()
plt.plot(xs,density0_post(xs),color='black')
plt.fill_between(xs,density1_post(xs),color='gray')
plt.title('After Matching')
plt.xlabel('Propensity Score')
plt.ylabel('Density')
plt.show()