dml-dev 0.1.2

DoubleML build, estimation, plotting, and utility pipelines.

DML Pipeline

Reusable build, estimation, plotting, and utility code for DoubleML-style program evaluation workflows.

The package keeps project-specific configuration in project_config/ and reusable pipeline code in dml_code/. The published distribution includes the code under dml_code/src and dml_code/pipeline, plus the example project configuration files.

Install

pip install dml-dev

For local development from a checkout:

python -m pip install -e '.[dev]'

Commands

After installation, the package exposes two command line entrypoints:

dml-build example_program
dml-estimate synthetic_example

The same steps can also be run as modules:

python -m dml_code.pipeline.step1_build example_program
python -m dml_code.pipeline.step2_estimate synthetic_example

Workflow

1. Build an analysis dataset from source parquet files and program registry entries.
2. Estimate treatment effects from a YAML experiment definition.
3. Write raw logs, diagnostics, plots, and tables to the configured output locations.

project_config/ + source data
        |
        v
dml_code.pipeline.step1_build
        |
        v
processed analysis data
        |
        v
dml_code.pipeline.step2_estimate
        |
        v
outputs/raw, outputs/plots, outputs/tables

Project Configuration

Most project setup happens in project_config/.

• project_config/build_spec.py: databank inputs, columns to carry through, relative-time columns, and generated features.
• project_config/registries/programs.py: program LazyFrames with canonical join, treatment, and observation columns.
• project_config/registries/covariate_sets.py: reusable covariate lists and categorical covariate declarations.
• project_config/registries/filter_sets.py: reusable dataframe filters.
• project_config/registries/models.py: named outcome and propensity learners.
• project_config/experiments/*.yaml: experiment definitions for estimation.

The reusable implementation lives in:

• dml_code/pipeline/: runnable build and estimation steps.
• dml_code/src/: shared helpers for building, estimating, paths, outputs, and logging.

Publishing Check

Build and validate the package locally with:

python -m build
python -m twine check dist/*