API Reference

CaML Core

from caml import *

Core functionality of CaML for estimating causal effects with cross-sectional data.

CamlCATE	The CamlCATE class represents an opinionated framework of Causal Machine Learning techniques for estimating highly accurate conditional average treatment effects (CATEs).
FastOLS	FastOLS is an optimized implementation of the OLS estimator designed specifically with treatment effect estimation in mind.

from caml.extensions.synthetic_data import *

Synthetic data generation utilities for CaML.

SyntheticDataGenerator	Generate highly flexible synthetic data for use in causal inference and CaML testing.
make_partially_linear_dataset_simple	Simulate data generating process from a partially linear model with a simple 1 or 2 dimensional CATE function.
make_partially_linear_dataset_constant	Simulate a data generating process from a partially linear model with a constant treatment effect (ATE only).
make_fully_heterogeneous_dataset	Simulate data generating process from an interactive regression model with fully heterogenous treatment effects.

from caml.extensions.plots import *

Plotting utilities for CaML.

cate_histogram_plot	Plots a histogram the estimated CATEs.
cate_line_plot	Plots a line plot of the ordered estimated CATEs as a rolling mean with optional confidence intervals.
cate_true_vs_estimated_plot	Plots a scatter plot of the estimated CATEs against the true CATEs.

Developer tools for CaML.

generics.experimental	Decorator to mark functions or classes as experimental.
generics.timer	Decorator to measure the execution time of a function or method, logged at DEBUG level.
generics.maybe_jit	Decorator to JIT compile a function using JAX, if available.
generics.PandasConvertibleDataFrame	Protocol for DataFrame-like objects that are pandas convertible.
logging.configure_logging	Configure logging for the entire application.
logging.set_log_level	Change the logging level after initial configuration.