params

[github-actions]

Rath/services/causal-service/algorithms/dowhy/ExplainData.py

Line 219 in d2cabfe

# TODO: params

    graph = constructPAG(fields, causalModel)
    print('treat:', treatment)
    results = []
    
    def testModel(results, model):
        # model.view_model()
        estimand = model.identify_effect(proceed_when_unidentifiable=True)
        methods = {
            'psm': 'backdoor.propensity_score_matching',
            'pss': 'backdoor.propensity_score_stratification',
            'psw': 'backdoor.propensity_score_weighting',
            'lr': 'backdoor.linear_regression',
            'glm': 'backdoor.generalized_linear_model',
            'iv': 'iv.instrumental_variable',
            'iv/rd': 'iv.regression_discontinuity'
        }
        tmp = lambda df: satisfy(df, groups.current)
        satCurrent, satOther = satisfy(dataSource, groups.current), satisfy(dataSource, groups.other)
        method = 'lr'
        if methods[method].startswith('backdoor.propensity_score_'):
            for treat in model._treatment:
                filters = [f for f in groups.current.predicates if f.fid == treat]
                tmp = IDoWhy.IRInsightExplainSubspace(predicates=filters)
                model._data = model._data.assign(**{treat: satisfy(transData, tmp) })
        estimate = model.estimate_effect(
            estimand,
            methods[method],
            target_units=lambda df: inferDiff(satCurrent, satOther), # satisfy(self.dataSource, groups.current), satisfy(df, groups.other)),
            # evaluate_effect_strength=True,
            )
        results.append(IDoWhy.LinkInfo(
            src=f.fid,
            tar=measures[0].fid,
            src_type=2,
            tar_type=1,
            description=IDoWhy.LinkInfoDescription(key='', data={'estimate': str(estimate)}),
            responsibility=significance_value(estimate.value, var=1.)
        ))
        # TODO: params
        if estimate.value > 0:
            print("f===========", f.fid)
            print("target_units=\n", dataSource[tmp(transData)])
            print('unobserved f = ', f, '\n', estimate)
    
    for e in adj[f_ind[measures[0].fid]]:
        if e['src_type'] in [-1, 2]:
            # TODO:
            pass
    
    # General: use origin graph 
    # Fallback: without graph, any variable can be used as common_cause
    for f in fields:
        if f.fid not in dimensions and f.fid not in [f.fid for f in measures]:
            # common_causes = [f.fid]
            # effect_modifiers = [f.fid]
            effect_modifiers = [f.fid]
            # TODO: if edges in graph
            model = dowhy.CausalModel(
                data=transData,
                # treatment=[d for d in dimensions if flipped or not compare(current.get(d, None), other.get(d, None))],
                common_causes=[f.fid],
                treatment=treatment,
                outcome=[measures[0].fid],
                # instruments=[], # Z, causes of treatment, no confounding for the effect of Z on outcome
                # effect_modifiers=effect_modifiers, # causes of outcome other than treatment
                # graph=graph,
                identify_vars=True
            )
            testModel(results, model)
    
    return results

def significance_value(x: float, var: float=1.):
    import scipy.stats as st
    """
        x (float): X - EX
        var (float): σ(X)
    """
    print("x = ", x)
    print("norm cdf =", st.norm.cdf(abs(x)))
    return 2 * st.norm.cdf(abs(x), scale=var) - 1

def ExplainData(props: IDoWhy.IRInsightExplainProps) -> tp.List[IDoWhy.IRInsightExplainResult]:
    session = ExplainDataSession(props.data, props.fields)
    session.g_gml = constructPAG(props.fields, props.causalModel)
    session.updateModel(props.view.dimensions, props.view.measures, props.groups)
    session.identitifyEstimand()
    session.estimateEffect(props.groups)
    results = []
    try:
        descrip_data = {
            'data': inferInfo(session),
            'target estimand': session.estimate.target_estimand.__str__(),
            'realized estimand': session.estimate.realized_estimand_expr,
            'target units': session.estimate.estimator.target_units_tostr() if hasattr(session.estimate, "estimator") else None,
            'mean value of estimation': session.estimate.value,
            'effect estimates': session.estimate.cate_estimates if hasattr(session.estimate, "cate_estimates") else None,
        }
        if hasattr(session.estimate, "estimator"):
            if session.estimate.estimator._significance_test:
                descrip_data['p-value'] = session.estimate.test_stat_significance()
                # session.estimate.estimator.signif_results_tostr(session.estimate.test_stat_significance())
            if session.estimate.estimator._confidence_intervals:
                descrip_data['confidence interval'], [session.estimate.estimator.confidence_level,
                                                                session.estimate.get_confidence_intervals()]
        if session.estimate.conditional_estimates is not None:
            descrip_data['conditional estimates'] = str(session.estimate.conditional_estimates)
        if session.estimate.effect_strength is not None:
            descrip_data['change in outcome attributable to treatment'] = session.estimate.effect_strength["fraction-effect"]
        print("descrip_data=", descrip_data)
        descrip_data['desc_by'] = 'ExplainData'
        results.append(IDoWhy.LinkInfo(
            src=props.view.dimensions[0], tar=props.view.measures[0].fid, src_type=-1, tar_type=1,
            description=IDoWhy.LinkInfoDescription(key='', data=descrip_data),
            responsibility=significance_value(session.estimate.value, var=1.)
        ))
    except Exception as e:
        print(str(e), file=sys.stderr)
    
    results.extend(explainData(props))
    # print("results =", results)
    
    return IDoWhy.IRInsightExplainResult(
        causalEffects=results

[github-actions]

Closed in fe41774

[github-actions]

Closed in fe41774