causal_agent.methods.diff_in_means package

Submodules

causal_agent.methods.diff_in_means.diagnostics module

Basic descriptive statistics for Difference in Means.

causal_agent.methods.diff_in_means.diagnostics.run_dim_diagnostics(df, treatment, outcome)[source]

Calculates basic descriptive statistics for treatment and control groups.

Parameters:

  • df (DataFrame) – Input DataFrame (should already be filtered for NaNs in treatment/outcome).

  • treatment (str) – Name of the binary treatment variable column.

  • outcome (str) – Name of the outcome variable column.

Returns:

Dictionary containing group means, standard deviations, and counts.

Return type:

Dict[str, Any]

causal_agent.methods.diff_in_means.estimator module

Difference in Means / Simple Linear Regression Estimator.

Estimates the Average Treatment Effect (ATE) by comparing the mean outcome between the treated and control groups. This is equivalent to a simple OLS regression of the outcome on the treatment indicator.

Assumes no confounding (e.g., suitable for RCT data).

causal_agent.methods.diff_in_means.estimator.estimate_effect(df, treatment, outcome, query=None, llm=None, **kwargs)[source]

Estimates the causal effect using Difference in Means (via OLS).

Ignores any provided covariates.

Parameters:

  • df (DataFrame) – Input DataFrame.

  • treatment (str) – Name of the binary treatment variable column (should be 0 or 1).

  • outcome (str) – Name of the outcome variable column.

  • query (str | None) – Optional user query for context.

  • llm (langchain.chat_models.base.BaseChatModel | None) – Optional Language Model instance.

  • **kwargs – Additional keyword arguments (ignored).

Returns:

  • ‘effect_estimate’: The difference in means (treatment coefficient).

  • ’p_value’: The p-value associated with the difference.

  • ’confidence_interval’: The 95% confidence interval for the difference.

  • ’standard_error’: The standard error of the difference.

  • ’formula’: The regression formula used.

  • ’model_summary’: Summary object from statsmodels.

  • ’diagnostics’: Basic group statistics.

  • ’interpretation’: LLM interpretation.

Return type:

Dictionary containing estimation results

causal_agent.methods.diff_in_means.llm_assist module

LLM assistance functions for Difference in Means analysis.

causal_agent.methods.diff_in_means.llm_assist.interpret_dim_results(results, diagnostics, treatment_var, llm=None)[source]

Use LLM to interpret Difference in Means results.

Parameters:

  • results (RegressionResultsWrapper) – Fitted statsmodels OLS results object (from outcome ~ treatment).

  • diagnostics (Dict[str, Any]) – Dictionary of diagnostic results (group stats).

  • treatment_var (str) – Name of the treatment variable.

  • llm (langchain.chat_models.base.BaseChatModel | None) – Optional LLM model instance.

Returns:

String containing natural language interpretation.

Return type:

str