cem 0.1.4

A Python implmentation of Coarsened Exact Matching for causal inference

cem

A Python implmentation of Coarsened Exact Matching (CEM). This is a poor mans version of the original R-package [1]. 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

from cem import CEM

boston = load_boston()
...
df

+----+---------+------+---------+--------+-------+-------+-------+--------+-------+-------+-----------+--------+---------+--------+
|    |    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 |
+----+---------+------+---------+--------+-------+-------+-------+--------+-------+-------+-----------+--------+---------+--------+

c = CEM(df, "CHAS", "MEDV")

# "bins" can be an int (number of quantiles or equal width bins) or sequence of scalars (quantiles for "qcut" or bin edges for "cut")
schema = {'CRIM': {'bins': 3, 'method': 'qcut'},
          'ZN': {'bins': 4, 'method': 'cut'},
          'INDUS': {'bins': 4, 'method': 'cut'},
          'NOX': {'bins': 4, 'method': 'cut'},
          'RM': {'bins': 4, 'method': 'cut'},
          'AGE': {'bins': 5, 'method': 'cut'},
          'DIS': {'bins': 5, 'method': 'cut'},
          'RAD': {'bins': 6, 'method': 'cut'},
          'TAX': {'bins': 4, 'method': 'cut'},
          'PTRATIO': {'bins': 5, 'method': 'cut'},
          'B': {'bins': 3, 'method': 'cut'},
          'LSTAT': {'bins': 2, 'method': 'cut'}}

# Check the multidimensional (L2) imbalance before and after matching
c.imbalance() # 0.96
c.imbalance(schema) # 0.67

# Get the weights for each example after matching using the coarsening schema
weights = c.match(schema)
weights[weights > 0]

+-----+----------+
|     |  weights |
+=====+==========+
| 142 | 1        |
+-----+----------+
| 143 | 0.504762 |
+-----+----------+
| 144 | 0.504762 |
+-----+----------+
| 147 | 0.504762 |
+-----+----------+
| 148 | 0.504762 |
+-----+----------+
| 149 | 0.504762 |
+-----+----------+
| 150 | 1.2619   |
+-----+----------+
| 151 | 1.2619   |
+-----+----------+
| 154 | 1        |
+-----+----------+
...


# ..perform weighted regression or weighted difference of means to find your treatment effect

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.

(Unfinished) Documentation: https://cem-coarsened-exact-matching-for-causal-inference.readthedocs.io.