Causal Inference with Generalized Propensity Score
pycausalgps is a Python library for implementing matching on generalized propensity scores with continuous exposures. We have developed an innovative approach for estimating causal effects using observational data in settings with continuous exposures and introduced a new framework for GPS caliper matching that jointly matches on both the estimated GPS and exposure levels to fully adjust for confounding bias.
Please refer to the documentation for setting up the enviorment.
To install the latest release from PyPI, run:
pip install pycausalgpsTo install the latest development version from GitHub, run:
git clone https://github.com/NSAPH-Software/pycausalgps
cd pycausalgps
pip install .from pycausalgps.base.utils import generate_syn_pop
data = generate_syn_pop(sample_size=1000,
seed_val=456,
outcome_sd=0.25,
gps_spec=1,
cova_spec=2)from pycausalgps.base.utils import generate_syn_pop
from pycausalgps.base import GeneralizedPropensityScore
params = {"gps_density": "normal",
"exposure_column": "treat",
"covariate_column_num": ["cf1",
"cf2",
"cf3",
"cf4",
"cf6"],
"covariate_column_cat": ["cf5"],
"libs":{
"xgboost":{
"n_estimators": 100,
"max_depth": 3,
"learning_rate": 0.1,
"test_rate": 0.2,
"random_state": 42
}
}
}
data = generate_syn_pop(sample_size=1000,
seed_val=456,
outcome_sd=0.25,
gps_spec=1,
cova_spec=2)
gps = GeneralizedPropensityScore(data, params)- Weighting approach
from pycausalgps.pseudo_population import PseudoPopulation
gps_params = {"gps_density": "normal",
"exposure_column": "treat",
"covariate_column_num": ["cf1",
"cf2",
"cf3",
"cf4",
"cf6"],
"covariate_column_cat": ["cf5"],
"libs":{
"xgboost":{
"n_estimators": 100,
"max_depth": 3,
"learning_rate": 0.1,
"test_rate": 0.2,
"random_state": 42
}
}
}
data = generate_syn_pop(sample_size=1000,
seed_val=456,
outcome_sd=0.25,
gps_spec=1,
cova_spec=2)
gps = GeneralizedPropensityScore(data, gps_params)
results = gps.get_results()
gps_data = {
'data' : results.get("data"),
'gps_density' : results.get("gps_density")
}
pspop_params = {"approach" : "weighting",
"exposure_column": "treat",
"covariate_column_num": ["cf1",
"cf2",
"cf3",
"cf4",
"cf6"],
"covariate_column_cat": ["cf5"],}
pspop = PseudoPopulation(data=data,
gps_data=gps_data,
params=pspop_params)- Matching approach
from pycausalgps.base.utils import generate_syn_pop
from pycausalgps.gps import GeneralizedPropensityScore
from pycausalgps.pseudo_population import PseudoPopulation
gps_params = {"gps_density": "normal",
"exposure_column": "treat",
"covariate_column_num": ["cf1",
"cf2",
"cf3",
"cf4",
"cf6"],
"covariate_column_cat": ["cf5"],
"libs":{
"xgboost":{
"n_estimators": 100,
"max_depth": 3,
"learning_rate": 0.1,
"test_rate": 0.2,
"random_state": 42
}
}
}
data = generate_syn_pop(sample_size=500,
seed_val=456,
outcome_sd=0.25,
gps_spec=1,
cova_spec=2)
gps = GeneralizedPropensityScore(data, gps_params)
results = gps.get_results()
gps_data = {
'data' : results.get("data"),
'gps_density' : results.get("gps_density"),
'gps_minmax': results.get("gps_minmax")
}
pspop_params = {"approach" : "matching",
"exposure_column": "treat",
"covariate_column_num": ["cf1",
"cf2",
"cf3",
"cf4",
"cf6"],
"covariate_column_cat": ["cf5"],
"control_params": {"caliper": 1.0,
"scale": 0.5,
"dist_measure": "l1",
"bin_seq": None},
"run_params": {"n_thread": 12,
"chunk_size": 500},
}
pspop = PseudoPopulation(data=data,
gps_data=gps_data,
params=pspop_params)Please note that the CausalGPS project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.
Documentation is hosted at https://nsaph-software.github.io/pycausalgps/