Packages

package causal

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Type Members

  1. abstract class BaseDiffInDiffEstimator extends Estimator[DiffInDiffModel] with DiffInDiffEstimatorParams
  2. trait CacheOps[T] extends AnyRef
  3. class DiffInDiffEstimator extends BaseDiffInDiffEstimator with ComplexParamsWritable with Wrappable with SynapseMLLogging
  4. trait DiffInDiffEstimatorParams extends Params with HasTreatmentCol with HasOutcomeCol with HasPostTreatmentCol
  5. class DiffInDiffModel extends Model[DiffInDiffModel] with HasUnitCol with HasTimeCol with Wrappable with ComplexParamsWritable with SynapseMLLogging
  6. case class DiffInDiffSummary(treatmentEffect: Double, standardError: Double, timeWeights: Option[DVector] = None, timeIntercept: Option[Double] = None, timeRMSE: Option[Double] = None, unitWeights: Option[DVector] = None, unitIntercept: Option[Double] = None, unitRMSE: Option[Double] = None, zeta: Option[Double] = None, lossHistoryTimeWeights: Option[List[Double]] = None, lossHistoryUnitWeights: Option[List[Double]] = None) extends Product with Serializable
  7. class DoubleMLEstimator extends Estimator[DoubleMLModel] with ComplexParamsWritable with DoubleMLParams with SynapseMLLogging with Wrappable

    Double ML estimators.

    Double ML estimators. The estimator follows the two stage process, where a set of nuisance functions are estimated in the first stage in a cross-fitting manner and a final stage estimates the average treatment effect (ATE) model. Our goal is to estimate the constant marginal ATE Theta(X)

    In this estimator, the ATE is estimated by using the following estimating equations: .. math :: Y - \\E[Y | X, W] = \\Theta(X) \\cdot (T - \\E[T | X, W]) + \\epsilon

    Thus if we estimate the nuisance functions :math:q(X, W) = \\E[Y | X, W] and :math:f(X, W)=\\E[T | X, W] in the first stage, we can estimate the final stage ate for each treatment t, by running a regression, minimizing the residual on residual square loss, estimating Theta(X) is a final regression problem, regressing tilde{Y} on X and tilde{T})

    .. math :: \\hat{\\theta} = \\arg\\min_{\\Theta}\ \E_n\\left[ (\\tilde{Y} - \\Theta(X) \\cdot \\tilde{T})^2 \\right]

    Where \\tilde{Y}=Y - \\E[Y | X, W] and :math:\\tilde{T}=T-\\E[T | X, W] denotes the residual outcome and residual treatment.

    The nuisance function :math:q is a simple machine learning problem and user can use setOutcomeModel to set an arbitrary sparkML model that is internally used to solve this problem

    The problem of estimating the nuisance function :math:f is also a machine learning problem and user can use setTreatmentModel to set an arbitrary sparkML model that is internally used to solve this problem.

  8. class DoubleMLModel extends Model[DoubleMLModel] with DoubleMLParams with ComplexParamsWritable with Wrappable with SynapseMLLogging

    Model produced by DoubleMLEstimator.

  9. trait DoubleMLParams extends Params with HasTreatmentCol with HasOutcomeCol with HasFeaturesCol with HasMaxIter with HasWeightCol with HasParallelismInjected
  10. trait HasMaxDepth extends Params
  11. trait HasMinSampleLeaf extends Params
  12. trait HasNumTrees extends Params
  13. trait HasOutcomeCol extends Params
  14. trait HasPostTreatmentCol extends Params
  15. trait HasTimeCol extends Params
  16. trait HasTreatmentCol extends Params
  17. trait HasUnitCol extends Params
  18. class OrthoForestDMLEstimator extends Estimator[OrthoForestDMLModel] with ComplexParamsWritable with OrthoForestDMLParams with Wrappable with SynapseMLLogging with HasOutcomeCol
  19. class OrthoForestDMLModel extends Model[OrthoForestDMLModel] with OrthoForestDMLParams with ComplexParamsWritable with Wrappable with SynapseMLLogging

    Model produced by OrthoForestDMLEstimator.

  20. trait OrthoForestDMLParams extends DoubleMLParams with HasNumTrees with HasMaxDepth with HasMinSampleLeaf with HasOutputCol
  21. class OrthoForestVariableTransformer extends Transformer with HasOutputCol with DefaultParamsWritable with Wrappable with SynapseMLLogging

    Transform the outcome residual and treatment residual.

    Transform the outcome residual and treatment residual. \E_n\left[ \left(\tilde{Y} - \theta(X) \cdot \tilde{T}\right)2 \right] = \E_n\left[ \tilde{T}2 \left(\frac{\tilde{Y}}{\tilde{T}} - \theta(X)\right)^2 \right] The latter corresponds to a weighted regression problem, where the target label is :math:\tilde{Y}/\tilde{T}, the features are X and the weight of each sample is :math:\tilde{T}^2. Any regressor that accepts sample weights can be used as a final model, e.g.:

  22. class ResidualTransformer extends Transformer with HasOutputCol with DefaultParamsWritable with Wrappable with SynapseMLLogging

    Compute the differences between observed and predicted values of data.

    Compute the differences between observed and predicted values of data. for classification, we compute residual as "observed - probability($(classIndex))" for regression, we compute residual as "observed - prediction"

  23. class SyntheticControlEstimator extends BaseDiffInDiffEstimator with SyntheticEstimator with SyntheticEstimatorParams with ComplexParamsWritable with Wrappable
  24. class SyntheticDiffInDiffEstimator extends BaseDiffInDiffEstimator with SyntheticEstimator with SyntheticDiffInDiffEstimatorParams with ComplexParamsWritable with Wrappable
  25. trait SyntheticDiffInDiffEstimatorParams extends SyntheticEstimatorParams
  26. trait SyntheticEstimator extends SynapseMLLogging
  27. trait SyntheticEstimatorParams extends Params with HasUnitCol with HasTimeCol with HasMaxIter with HasStepSize with HasTol

Value Members

  1. object BDVCacheOps extends CacheOps[DenseVector[Double]]
  2. object ConstantColumns
  3. object DVectorCacheOps extends CacheOps[DVector]
  4. object DiffInDiffEstimator extends ComplexParamsReadable[DiffInDiffEstimator] with Serializable
  5. object DiffInDiffModel extends ComplexParamsReadable[DiffInDiffModel] with Serializable
  6. object DoubleMLEstimator extends ComplexParamsReadable[DoubleMLEstimator] with Serializable
  7. object DoubleMLModel extends ComplexParamsReadable[DoubleMLModel] with Serializable
  8. object OrthoForestDMLEstimator extends ComplexParamsReadable[OrthoForestDMLEstimator] with Serializable
  9. object OrthoForestDMLModel extends ComplexParamsReadable[OrthoForestDMLModel] with Serializable
  10. object OrthoForestVariableTransformer extends DefaultParamsReadable[OrthoForestVariableTransformer] with Serializable
  11. object ResidualTransformer extends DefaultParamsReadable[ResidualTransformer] with Serializable
  12. object SyntheticControlEstimator extends ComplexParamsReadable[SyntheticControlEstimator] with Serializable
  13. object SyntheticDiffInDiffEstimator extends ComplexParamsReadable[SyntheticDiffInDiffEstimator] with Serializable
  14. object SyntheticEstimator

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