Coarsened Exact Matching for Causal Inference
Project description
cem: Coarsened Exact Matching for Causal Inference
cem is a lightweight library for Coarsened Exact Matching (CEM) and is essentially a poor man's version of the original R-package [1]. CEM is a matching technique used to reduce covariate imbalance, which would otherwise lead to treatment effect estimates that are sensitive to model specification. By removing and/or reweighting certain observations via CEM, one can arrive at treatment effect estimates that are more stable than those found using other matching techniques like propensity score matching. The L1 and L2 multivariate imbalance measures are implemented as described in [2]. I make no claim to originality and thank the authors for their research.
Usage
Load the data
from cem import match
from cem import coarsen
from cem.imbalance import L1
import statsmodels.api as sm
boston = load_boston()
O = "MEDV" # outcome variable
T = "CHAS" # treatment variable
y = boston[O]
X = boston.drop(columns=O)
CRIM | ZN | INDUS | CHAS | NOX | RM | AGE | DIS | RAD | TAX | PTRATIO | B | LSTAT | MEDV | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 0.00632 | 18 | 2.31 | 0 | 0.538 | 6.575 | 65.2 | 4.09 | 1 | 296 | 15.3 | 396.9 | 4.98 | 24 |
1 | 0.02731 | 0 | 7.07 | 0 | 0.469 | 6.421 | 78.9 | 4.9671 | 2 | 242 | 17.8 | 396.9 | 9.14 | 21.6 |
2 | 0.02729 | 0 | 7.07 | 0 | 0.469 | 7.185 | 61.1 | 4.9671 | 2 | 242 | 17.8 | 392.83 | 4.03 | 34.7 |
3 | 0.03237 | 0 | 2.18 | 0 | 0.458 | 6.998 | 45.8 | 6.0622 | 3 | 222 | 18.7 | 394.63 | 2.94 | 33.4 |
4 | 0.06905 | 0 | 2.18 | 0 | 0.458 | 7.147 | 54.2 | 6.0622 | 3 | 222 | 18.7 | 396.9 | 5.33 | 36.2 |
Automatic Coarsening
First we coarsen the data in an automatic fashion to get a baseline imbalance. Be sure to drop the column containing your outcome variable prior to coarsening/matching. coarsen
optionally takes a list of columns you'd like to auto-coarsen, ignoring the rest.
# coarsen predictor variables
X_coarse = coarsen(X, T, "l1")
# match observations
weights = match(X_coarse, T)
# calculate weighted imbalance
L1(X_coarse, weights)
Informed Coarsening
It's recommended to coarsen using pandas.cut
and pandas.qcut
, but you are free to coarsen your predictor variables however you wish.
# coarsen predictor variables
schema = {
'CRIM': (pd.cut, {'bins': 4}),
'ZN': (pd.qcut, {'q': 4}),
'INDUS': (pd.qcut, {'q': 4}),
'NOX': (pd.cut, {'bins': 5}),
'RM': (pd.cut, {'bins': 5}),
'AGE': (pd.cut, {'bins': 5}),
'DIS': (pd.cut, {'bins': 5}),
'RAD': (pd.cut, {'bins': 6}),
'TAX': (pd.cut, {'bins': 5}),
'PTRATIO': (pd.cut, {'bins': 6}),
'B': (pd.cut, {'bins': 5}),
'LSTAT': (pd.cut, {'bins': 5})
}
X_coarse = X.apply(lambda x: schema[x.name][0](x, **schema[x.name][1]) if x.name in schema else x)
# match observations
weights = match(X_coarse, T)
# calculate weighted imbalance
L1(X_coarse, weights)
# perform weighted regression
model = sm.WLS(y, sm.add_constant(X), weights=weights)
References
[1] Porro, Giuseppe & King, Gary & Iacus, Stefano. (2009). CEM: Software for Coarsened Exact Matching. Journal of Statistical Software. 30. 10.18637/jss.v030.i09.
[2] Iacus, S. M., King, G., and Porro, G. Multivariate matching methods that are monotonic imbalance bounding. Journal of the American Statistical Association 106, 493 (2011 2011), 345–361.
[3] Iacus, S. M., King, G., and Porro, G. Causal inference without balance checking: Coarsened exact matching. Political Analysis 20, 1 (2012), 1–24.
[4] King, G., and Zeng, L. The dangers of extreme counterfactuals. Political Analysis 14 (2006), 131–159.
[5] Ho, D., Imai, K., King, G., and Stuart, E. Matching as nonparametric preprocessing for reducing model dependence in parametric causal inference. Political Analysis 15 (2007), 199–236.
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.