addressing comments

core/src/main/scala/com/microsoft/azure/synapse/ml/causal/BaseDiffInDiffEstimator.scala

@@ -5,11 +5,12 @@ package com.microsoft.azure.synapse.ml.causal
 
 import com.microsoft.azure.synapse.ml.causal.linalg.DVector
 import com.microsoft.azure.synapse.ml.codegen.Wrappable
+import com.microsoft.azure.synapse.ml.core.schema.DatasetExtensions
 import com.microsoft.azure.synapse.ml.logging.{FeatureNames, SynapseMLLogging}
 import com.microsoft.azure.synapse.ml.param.DataFrameParam
 import org.apache.spark.SparkException
 import org.apache.spark.ml.feature.VectorAssembler
-import org.apache.spark.ml.param.ParamMap
+import org.apache.spark.ml.param.{ParamMap, Params}
 import org.apache.spark.ml.regression.LinearRegression
 import org.apache.spark.ml.util.Identifiable
 import org.apache.spark.ml.{ComplexParamsReadable, ComplexParamsWritable, Estimator, Model}
@@ -43,7 +44,7 @@ abstract class BaseDiffInDiffEstimator(override val uid: String)
 
   override def copy(extra: ParamMap): Estimator[DiffInDiffModel] = defaultCopy(extra)
 
-  private[causal] val interactionCol = "interaction"
+  private[causal] val findInteractionCol = DatasetExtensions.findUnusedColumnName("interaction") _
 
   private[causal] def fitLinearModel(df: DataFrame,
                                      featureCols: Array[String],
@@ -57,8 +58,10 @@ abstract class BaseDiffInDiffEstimator(override val uid: String)
       .map(new LinearRegression().setWeightCol)
       .getOrElse(new LinearRegression())
 
+    val featuresCol = DatasetExtensions.findUnusedColumnName("features", df)
+
     regression
-      .setFeaturesCol("features")
+      .setFeaturesCol(featuresCol)
       .setLabelCol(getOutcomeCol)
       .setFitIntercept(fitIntercept)
       .setLoss("squaredError")
@@ -162,3 +165,8 @@ class DiffInDiffModel(override val uid: String)
 }
 
 object DiffInDiffModel extends ComplexParamsReadable[DiffInDiffModel]
+
+trait DiffInDiffEstimatorParams extends Params
+  with HasTreatmentCol
+  with HasOutcomeCol
+  with HasPostTreatmentCol

core/src/main/scala/com/microsoft/azure/synapse/ml/causal/DiffInDiffEstimator.scala

@@ -4,6 +4,7 @@
 package com.microsoft.azure.synapse.ml.causal
 
 import com.microsoft.azure.synapse.ml.codegen.Wrappable
+import com.microsoft.azure.synapse.ml.core.schema.DatasetExtensions
 import com.microsoft.azure.synapse.ml.logging.{FeatureNames, SynapseMLLogging}
 import org.apache.spark.ml.util.Identifiable
 import org.apache.spark.ml.{ComplexParamsReadable, ComplexParamsWritable}
@@ -22,6 +23,7 @@ class DiffInDiffEstimator(override val uid: String)
   def this() = this(Identifiable.randomUID("DiffInDiffEstimator"))
 
   override def fit(dataset: Dataset[_]): DiffInDiffModel = logFit({
+    val interactionCol = findInteractionCol(dataset.columns.toSet)
     val postTreatment = col(getPostTreatmentCol)
     val treatment = col(getTreatmentCol)
     val outcome = col(getOutcomeCol)

core/src/main/scala/com/microsoft/azure/synapse/ml/causal/SyntheticControlEstimator.scala

@@ -57,6 +57,8 @@ class SyntheticControlEstimator(override val uid: String)
 
     val indexedDf = df.join(unitIdx, df(getUnitCol) === unitIdx(getUnitCol), "left_outer")
 
+    val interactionCol = findInteractionCol(indexedDf.columns.toSet)
+
     val didData = indexedDf.select(
         col(getTimeCol),
         col(UnitIdxCol),

core/src/main/scala/com/microsoft/azure/synapse/ml/causal/SyntheticDiffInDiffEstimator.scala

@@ -70,6 +70,8 @@ class SyntheticDiffInDiffEstimator(override val uid: String)
     val indexedDf = df.join(timeIdx, df(getTimeCol) === timeIdx(getTimeCol), "left_outer")
       .join(unitIdx, df(getUnitCol) === unitIdx(getUnitCol), "left_outer")
 
+    val interactionCol = findInteractionCol(indexedDf.columns.toSet)
+
     val didData = indexedDf.select(
         col(UnitIdxCol),
         col(TimeIdxCol),

core/src/main/scala/com/microsoft/azure/synapse/ml/causal/SyntheticEstimatorParams.scala

@@ -3,11 +3,9 @@
 
 package com.microsoft.azure.synapse.ml.causal
 
-import org.apache.spark.ml.param.{IntParam, LongParam, Param, ParamValidators, Params}
+import org.apache.spark.ml.param.{DoubleParam, IntParam, LongParam, Param, ParamValidators, Params}
 import org.apache.spark.ml.param.shared.{HasMaxIter, HasStepSize, HasTol}
 
-import scala.util.Random
-
 trait SyntheticEstimatorParams extends Params
   with HasUnitCol
   with HasTimeCol
@@ -48,6 +46,15 @@ trait SyntheticEstimatorParams extends Params
   /** @group expertGetParam */
   def setLocalSolverThreshold(value: Long): this.type = set(localSolverThreshold, value)
 
+  final val epsilon = new DoubleParam(this, "epsilon",
+    "This value is added to the weights when we fit the final linear model for " +
+      "SyntheticControlEstimator and SyntheticDiffInDiffEstimator in order to avoid " +
+      "zero weights.", ParamValidators.gt(0d))
+
+  def getEpsilon: Double = $(epsilon)
+
+  def setEpsilon(value: Double): this.type = set(epsilon, value)
+
   def setMaxIter(value: Int): this.type = set(maxIter, value)
 
   def setStepSize(value: Double): this.type = set(stepSize, value)
@@ -59,6 +66,7 @@ trait SyntheticEstimatorParams extends Params
     tol -> 1E-3,
     maxIter -> 100,
     handleMissingOutcome -> "zero",
-    localSolverThreshold -> 1000 * 1000
+    localSolverThreshold -> 1000 * 1000,
+    epsilon -> 1E-10
   )
 }