API Quickstart — causalml documentation (original) (raw)

Working example notebooks are available in the example folder.

Propensity Score

Propensity Score Estimation

from causalml.propensity import ElasticNetPropensityModel

pm = ElasticNetPropensityModel(n_fold=5, random_state=42) ps = pm.fit_predict(X, y)

Propensity Score Matching

from causalml.match import NearestNeighborMatch, create_table_one

psm = NearestNeighborMatch(replace=False, ratio=1, random_state=42) matched = psm.match_by_group(data=df, treatment_col=treatment_col, score_cols=score_cols, groupby_col=groupby_col)

create_table_one(data=matched, treatment_col=treatment_col, features=covariates)

Average Treatment Effect (ATE) Estimation

Meta-learners and Uplift Trees

In addition to the Methodology section, you can find examples in the links below for Meta-Learner Algorithms and Tree-Based Algorithms

• Meta-learners (S/T/X/R): meta_learners_with_synthetic_data.ipynb
• Meta-learners (S/T/X/R) with multiple treatment: meta_learners_with_synthetic_data_multiple_treatment.ipynb
• Comparing meta-learners across simulation setups: benchmark_simulation_studies.ipynb
• Doubly Robust (DR) learner: dr_learner_with_synthetic_data.ipynb
• TMLE learner: validation_with_tmle.ipynb
• Uplift Trees: uplift_trees_with_synthetic_data.ipynb

from causalml.inference.meta import LRSRegressor from causalml.inference.meta import XGBTRegressor, MLPTRegressor from causalml.inference.meta import BaseXRegressor from causalml.inference.meta import BaseRRegressor from xgboost import XGBRegressor from causalml.dataset import synthetic_data

y, X, treatment, _, _, e = synthetic_data(mode=1, n=1000, p=5, sigma=1.0)

lr = LRSRegressor() te, lb, ub = lr.estimate_ate(X, treatment, y) print('Average Treatment Effect (Linear Regression): {:.2f} ({:.2f}, {:.2f})'.format(te[0], lb[0], ub[0]))

xg = XGBTRegressor(random_state=42) te, lb, ub = xg.estimate_ate(X, treatment, y) print('Average Treatment Effect (XGBoost): {:.2f} ({:.2f}, {:.2f})'.format(te[0], lb[0], ub[0]))

nn = MLPTRegressor(hidden_layer_sizes=(10, 10), learning_rate_init=.1, early_stopping=True, random_state=42) te, lb, ub = nn.estimate_ate(X, treatment, y) print('Average Treatment Effect (Neural Network (MLP)): {:.2f} ({:.2f}, {:.2f})'.format(te[0], lb[0], ub[0]))

xl = BaseXRegressor(learner=XGBRegressor(random_state=42)) te, lb, ub = xl.estimate_ate(X, treatment, y, e) print('Average Treatment Effect (BaseXRegressor using XGBoost): {:.2f} ({:.2f}, {:.2f})'.format(te[0], lb[0], ub[0]))

rl = BaseRRegressor(learner=XGBRegressor(random_state=42)) te, lb, ub = rl.estimate_ate(X=X, p=e, treatment=treatment, y=y) print('Average Treatment Effect (BaseRRegressor using XGBoost): {:.2f} ({:.2f}, {:.2f})'.format(te[0], lb[0], ub[0]))

More algorithms

Treatment optimization algorithms

We have developed Counterfactual Unit Selection and Counterfactual Value Estimator methods, please find the code snippet below and details in the following notebooks:

• counterfactual_unit_selection.ipynb
• counterfactual_value_optimization.ipynb

from causalml.optimize import CounterfactualValueEstimator from causalml.optimize import get_treatment_costs, get_actual_value

load data set and train test split

df_train, df_test = train_test_split(df) train_idx = df_train.index test_idx = df_test.index

some more code here to initiate and train the Model, and produce tm_pred

please refer to the counterfactual_value_optimization notebook for complete example

run the counterfactual calculation with TwoModel prediction

cve = CounterfactualValueEstimator(treatment=df_test['treatment_group_key'], control_name='control', treatment_names=conditions[1:], y_proba=y_proba, cate=tm_pred, value=conversion_value_array[test_idx], conversion_cost=cc_array[test_idx], impression_cost=ic_array[test_idx])

cve_best_idx = cve.predict_best() cve_best = [conditions[idx] for idx in cve_best_idx] actual_is_cve_best = df.loc[test_idx, 'treatment_group_key'] == cve_best cve_value = actual_value.loc[test_idx][actual_is_cve_best].mean()

labels = [ 'Random allocation', 'Best treatment', 'T-Learner', 'CounterfactualValueEstimator' ] values = [ random_allocation_value, best_ate_value, tm_value, cve_value ]

plot the result

plt.bar(labels, values)

Instrumental variables algorithms

• 2-Stage Least Squares (2SLS): iv_nlsym_synthetic_data.ipynb

Neural network based algorithms

• CEVAE: cevae_example.ipynb
• DragonNet: dragonnet_example.ipynb

Interpretation

Please see Interpretable Causal ML section

Validation

Please see Validation section

Synthetic Data Generation Process

Single Simulation

from causalml.dataset import *

Generate synthetic data for single simulation

y, X, treatment, tau, b, e = synthetic_data(mode=1) y, X, treatment, tau, b, e = simulate_nuisance_and_easy_treatment()

Generate predictions for single simulation

single_sim_preds = get_synthetic_preds(simulate_nuisance_and_easy_treatment, n=1000)

Generate multiple scatter plots to compare learner performance for a single simulation

scatter_plot_single_sim(single_sim_preds)

Visualize distribution of learner predictions for a single simulation

distr_plot_single_sim(single_sim_preds, kind='kde')

Multiple Simulations

from causalml.dataset import *

Generalize performance summary over k simulations

num_simulations = 12 preds_summary = get_synthetic_summary(simulate_nuisance_and_easy_treatment, n=1000, k=num_simulations)

Generate scatter plot of performance summary

scatter_plot_summary(preds_summary, k=num_simulations)

Generate bar plot of performance summary

bar_plot_summary(preds_summary, k=num_simulations)

Sensitivity Analysis

For more details, please refer to the sensitivity_example_with_synthetic_data.ipynb notebook.

from causalml.metrics.sensitivity import Sensitivity from causalml.metrics.sensitivity import SensitivitySelectionBias from causalml.inference.meta import BaseXLearner from sklearn.linear_model import LinearRegression

Calling the Base XLearner class and return the sensitivity analysis summary report

learner_x = BaseXLearner(LinearRegression()) sens_x = Sensitivity(df=df, inference_features=INFERENCE_FEATURES, p_col='pihat', treatment_col=TREATMENT_COL, outcome_col=OUTCOME_COL, learner=learner_x)

Here for Selection Bias method will use default one-sided confounding function and alpha (quantile range of outcome values) input

sens_sumary_x = sens_x.sensitivity_analysis(methods=['Placebo Treatment', 'Random Cause', 'Subset Data', 'Random Replace', 'Selection Bias'], sample_size=0.5)

Selection Bias: Alignment confounding Function

sens_x_bias_alignment = SensitivitySelectionBias(df, INFERENCE_FEATURES, p_col='pihat', treatment_col=TREATMENT_COL, outcome_col=OUTCOME_COL, learner=learner_x, confound='alignment', alpha_range=None)

Plot the results by rsquare with partial r-square results by each individual features

sens_x_bias_alignment.plot(lls_x_bias_alignment, partial_rsqs_x_bias_alignment, type='r.squared', partial_rsqs=True)

Feature Selection

For more details, please refer to the feature_selection.ipynb notebook and the associated paper reference by Zhao, Zhenyu, et al.

from causalml.feature_selection.filters import FilterSelect from causalml.dataset import make_uplift_classification

define parameters for simulation

y_name = 'conversion' treatment_group_keys = ['control', 'treatment1'] n = 100000 n_classification_features = 50 n_classification_informative = 10 n_classification_repeated = 0 n_uplift_increase_dict = {'treatment1': 8} n_uplift_decrease_dict = {'treatment1': 4} delta_uplift_increase_dict = {'treatment1': 0.1} delta_uplift_decrease_dict = {'treatment1': -0.1}

make a synthetic uplift data set

random_seed = 20200808 df, X_names = make_uplift_classification( treatment_name=treatment_group_keys, y_name=y_name, n_samples=n, n_classification_features=n_classification_features, n_classification_informative=n_classification_informative, n_classification_repeated=n_classification_repeated, n_uplift_increase_dict=n_uplift_increase_dict, n_uplift_decrease_dict=n_uplift_decrease_dict, delta_uplift_increase_dict = delta_uplift_increase_dict, delta_uplift_decrease_dict = delta_uplift_decrease_dict, random_seed=random_seed )

Feature selection with Filter method

filter_f = FilterSelect() method = 'F' f_imp = filter_f.get_importance(df, X_names, y_name, method, treatment_group = 'treatment1') print(f_imp)

Use likelihood ratio test method

method = 'LR' lr_imp = filter_f.get_importance(df, X_names, y_name, method, treatment_group = 'treatment1') print(lr_imp)

Use KL divergence method

method = 'KL' kl_imp = filter_f.get_importance(df, X_names, y_name, method, treatment_group = 'treatment1', n_bins=10) print(kl_imp)