Add Bayesian instrumental variable estimation

causalpy/data/AJR2001.csv

@@ -0,0 +1,65 @@
+longname,shortnam,logmort0,risk,loggdp,campaign,source0,slave,latitude,neoeuro,asia,africa,other,edes1975,campaignsj,campaignsj2,mortnaval1,logmortnaval1,mortnaval2,logmortnaval2,mortjam,logmortjam,logmortcap250,logmortjam250,wandcafrica,malfal94,wacacontested,mortnaval2250,logmortnaval2250,mortnaval1250,logmortnaval1250
+Angola,AGO,5.6347895,5.3600001,7.77,1,0,0,0.1367,0,0,1,0,0.0,1,1,280.0,5.6347895,280.0,5.6347895,280.0,5.6347895,5.521461,5.521461,1,0.94999999,1,250.0,5.521461,250.0,5.521461
+Argentina,ARG,4.232656,6.3899999,9.1300001,1,0,0,0.37779999,0,0,0,0,90.0,1,1,15.07,2.7127061,30.5,3.4177268,56.5,4.0342407,4.232656,4.0342407,0,0.0,0,30.5,3.4177268,15.07,2.7127061
+Australia,AUS,2.1459312,9.3199997,9.8999996,0,0,0,0.30000001,1,0,0,1,99.0,0,1,8.5500002,2.1459312,8.5500002,2.1459312,8.5500002,2.1459312,2.1459312,2.1459312,0,0.0,0,8.5500002,2.1459312,8.5500002,2.1459312
+Burkina Faso,BFA,5.6347895,4.4499998,6.8499999,1,0,0,0.1444,0,0,1,0,0.0,1,1,280.0,5.6347895,280.0,5.6347895,280.0,5.6347895,5.521461,5.521461,1,0.94999999,1,250.0,5.521461,250.0,5.521461
+Bangladesh,BGD,4.2684379,5.1399999,6.8800001,1,1,0,0.2667,0,1,0,0,0.0,1,1,71.410004,4.2684379,71.410004,4.2684379,71.410004,4.2684379,4.2684379,4.2684379,0,0.12008,0,71.410004,4.2684379,71.410004,4.2684379
+Bahamas,BHS,4.4426513,7.5,9.29,0,0,0,0.2683,0,0,0,0,10.0,0,0,85.0,4.4426513,85.0,4.4426513,85.0,4.4426513,4.4426513,4.4426513,0,,0,85.0,4.4426513,85.0,4.4426513
+Bolivia,BOL,4.2626801,5.6399999,7.9299998,1,0,0,0.18889999,0,0,0,0,30.000002,1,1,,,93.25,4.535284,56.5,4.0342407,4.2626801,4.0342407,0,0.00165,0,93.25,4.535284,,
+Brazil,BRA,4.2626801,7.9099998,8.7299995,1,0,0,0.1111,0,0,0,0,55.0,1,1,15.07,2.7127061,30.5,3.4177268,56.5,4.0342407,4.2626801,4.0342407,0,0.035999998,0,30.5,3.4177268,15.07,2.7127061
+Canada,CAN,2.7788193,9.7299995,9.9899998,0,1,0,0.66670001,1,0,0,0,98.0,0,0,16.1,2.7788193,16.1,2.7788193,16.1,2.7788193,2.7788193,2.7788193,0,0.0,0,16.1,2.7788193,16.1,2.7788193
+Chile,CHL,4.232656,7.8200002,9.3400002,1,0,0,0.33329999,0,0,0,0,50.0,1,1,15.07,2.7127061,30.5,3.4177268,56.5,4.0342407,4.232656,4.0342407,0,0.0,0,30.5,3.4177268,15.07,2.7127061
+Cote d'Ivoire,CIV,6.5042882,7.0,7.4400001,1,0,0,0.0889,0,0,1,0,0.0,1,1,668.0,6.5042882,668.0,6.5042882,668.0,6.5042882,5.521461,5.521461,1,0.94999999,1,250.0,5.521461,250.0,5.521461
+Cameroon,CMR,5.6347895,6.4499998,7.5,1,0,0,0.066699997,0,0,1,0,0.0,1,1,280.0,5.6347895,280.0,5.6347895,280.0,5.6347895,5.521461,5.521461,1,0.94999999,1,250.0,5.521461,250.0,5.521461
+Congo,COG,5.480639,4.6799998,7.4200001,0,1,1,0.0111,0,0,1,0,0.0,0,0,240.0,5.480639,240.0,5.480639,240.0,5.480639,5.480639,5.480639,1,0.94999999,0,240.0,5.480639,240.0,5.480639
+Colombia,COL,4.2626801,7.3200002,8.8100004,1,0,0,0.044399999,0,0,0,0,25.0,1,1,,,93.25,4.535284,56.5,4.0342407,4.2626801,4.0342407,0,0.14637001,0,93.25,4.535284,,
+Costa Rica,CRI,4.3579903,7.0500002,8.79,1,0,0,0.1111,0,0,0,0,20.0,1,1,,,93.25,4.535284,62.200001,4.1303549,4.3579903,4.1303549,0,0.0,0,93.25,4.535284,,
+Dominican Re,DOM,4.8675346,6.1799998,8.3599997,0,0,0,0.2111,0,0,0,0,25.0,0,0,130.0,4.8675346,130.0,4.8675346,130.0,4.8675346,4.8675346,4.8675346,0,0.0,0,130.0,4.8675346,130.0,4.8675346
+Algeria,DZA,4.3592696,6.5,8.3900003,1,1,0,0.31110001,0,0,1,0,0.0,1,1,78.199997,4.3592696,78.199997,4.3592696,78.199997,4.3592696,4.3592696,4.3592696,0,0.0,0,78.199997,4.3592696,78.199997,4.3592696
+Ecuador,ECU,4.2626801,6.5500002,8.4700003,1,0,0,0.0222,0,0,0,0,30.000002,1,1,,,93.25,4.535284,56.5,4.0342407,4.2626801,4.0342407,0,0.11894999,0,93.25,4.535284,,
+Egypt,EGY,4.2165623,6.77,7.9499998,1,1,0,0.30000001,0,0,1,0,0.0,1,1,67.800003,4.2165623,67.800003,4.2165623,67.800003,4.2165623,4.2165623,4.2165623,0,0.0,0,67.800003,4.2165623,67.800003,4.2165623
+Ethiopia,ETH,3.2580965,5.73,6.1100001,1,1,0,0.0889,0,0,1,0,0.0,1,1,26.0,3.2580965,26.0,3.2580965,26.0,3.2580965,3.2580965,3.2580965,1,0.551,0,26.0,3.2580965,26.0,3.2580965
+Gabon,GAB,5.6347895,7.8200002,8.8999996,1,0,0,0.0111,0,0,1,0,0.0,1,1,280.0,5.6347895,280.0,5.6347895,280.0,5.6347895,5.521461,5.521461,1,0.94050002,1,250.0,5.521461,250.0,5.521461
+Ghana,GHA,6.5042882,6.27,7.3699999,1,1,0,0.0889,0,0,1,0,0.0,1,1,668.0,6.5042882,668.0,6.5042882,668.0,6.5042882,5.521461,5.521461,1,0.94999999,0,250.0,5.521461,250.0,5.521461
+Guinea,GIN,6.1800165,6.5500002,7.4899998,1,0,0,0.1222,0,0,1,0,0.0,1,1,483.0,6.1800165,483.0,6.1800165,483.0,6.1800165,5.521461,5.521461,1,0.94999999,1,250.0,5.521461,250.0,5.521461
+Gambia,GMB,7.2930179,8.2700005,7.27,1,1,0,0.1476,0,0,1,0,0.0,1,1,1470.0,7.2930179,1470.0,7.2930179,1470.0,7.2930179,5.521461,5.521461,1,0.94999999,0,250.0,5.521461,250.0,5.521461
+Guatemala,GTM,4.2626801,5.1399999,8.29,1,0,0,0.17,0,0,0,0,20.0,1,1,,,93.25,4.535284,56.5,4.0342407,4.2626801,4.0342407,0,0.0036000002,0,93.25,4.535284,,
+Guyana,GUY,3.4713452,5.8899999,7.9000001,0,0,0,0.055599999,0,0,0,0,2.0,0,0,32.18,3.4713452,32.18,3.4713452,32.18,3.4713452,3.4713452,3.4713452,0,0.49503002,0,32.18,3.4713452,32.18,3.4713452
+Hong Kong,HKG,2.7013612,8.1400003,10.05,0,0,0,0.24609999,0,1,0,0,0.0,1,1,14.9,2.7013612,14.9,2.7013612,14.9,2.7013612,2.7013612,2.7013612,0,0.0,0,14.9,2.7013612,14.9,2.7013612
+Honduras,HND,4.3579903,5.3200002,7.6900001,1,0,0,0.16670001,0,0,0,0,20.0,1,1,,,93.25,4.535284,62.200001,4.1303549,4.3579903,4.1303549,0,0.012,0,93.25,4.535284,,
+Haiti,HTI,4.8675346,3.73,7.1500001,0,0,0,0.2111,0,0,0,0,0.0,0,0,130.0,4.8675346,130.0,4.8675346,130.0,4.8675346,4.8675346,4.8675346,0,1.0,0,130.0,4.8675346,130.0,4.8675346
+Indonesia,IDN,5.1357985,7.5900002,7.3299999,1,1,0,0.055599999,0,1,0,0,0.0,1,1,170.0,5.1357985,170.0,5.1357985,170.0,5.1357985,5.1357985,5.1357985,0,0.17873,0,170.0,5.1357985,170.0,5.1357985
+India,IND,3.8842406,8.2700005,7.3299999,0,1,0,0.22220001,0,1,0,0,0.0,0,0,48.630001,3.8842406,48.630001,3.8842406,48.630001,3.8842406,3.8842406,3.8842406,0,0.23596001,0,48.630001,3.8842406,48.630001,3.8842406
+Jamaica,JAM,4.8675346,7.0900002,8.1899996,0,1,0,0.2017,0,0,0,0,10.0,0,1,130.0,4.8675346,130.0,4.8675346,130.0,4.8675346,4.8675346,4.8675346,0,0.0,0,130.0,4.8675346,130.0,4.8675346
+Kenya,KEN,4.9767337,6.0500002,7.0599999,0,1,1,0.0111,0,0,1,0,0.0,0,0,145.0,4.9767337,145.0,4.9767337,145.0,4.9767337,4.9767337,4.9767337,1,0.79799998,0,145.0,4.9767337,145.0,4.9767337
+Sri Lanka,LKA,4.2456341,6.0500002,7.73,0,1,0,0.077799998,0,1,0,0,0.0,0,1,69.800003,4.2456341,69.800003,4.2456341,69.800003,4.2456341,4.2456341,4.2456341,0,0.138,0,69.800003,4.2456341,69.800003,4.2456341
+Morocco,MAR,4.3592696,7.0900002,8.04,1,0,0,0.3556,0,0,1,0,1.0,1,1,78.199997,4.3592696,78.199997,4.3592696,78.199997,4.3592696,4.3592696,4.3592696,0,0.0,0,78.199997,4.3592696,78.199997,4.3592696
+Madagascar,MDG,6.2842088,4.4499998,6.8400002,1,1,0,0.22220001,0,0,1,0,0.0,1,1,536.03998,6.2842088,536.03998,6.2842088,536.03998,6.2842088,5.521461,5.521461,1,0.94999999,0,250.0,5.521461,250.0,5.521461
+Mexico,MEX,4.2626801,7.5,8.9399996,1,1,0,0.25560001,0,0,0,0,15.000001,1,1,71.0,4.2626801,71.0,4.2626801,71.0,4.2626801,4.2626801,4.2626801,0,0.00042,0,71.0,4.2626801,71.0,4.2626801
+Mali,MLI,7.986165,4.0,6.5700002,1,1,0,0.18889999,0,0,1,0,0.0,1,1,2940.0,7.986165,2940.0,7.986165,2940.0,7.986165,5.521461,5.521461,1,0.94050002,0,250.0,5.521461,250.0,5.521461
+Malta,MLT,2.7911651,7.23,9.4300003,0,1,0,0.3944,0,0,0,1,100.0,0,0,16.299999,2.7911651,16.299999,2.7911651,16.299999,2.7911651,2.7911651,2.7911651,0,,0,16.299999,2.7911651,16.299999,2.7911651
+Malaysia,MYS,2.8735647,7.9499998,8.8900003,0,1,0,0.025599999,0,1,0,0,0.0,0,1,17.700001,2.8735647,17.700001,2.8735647,17.700001,2.8735647,2.8735647,2.8735647,0,0.23331,0,17.700001,2.8735647,17.700001,2.8735647
+Niger,NER,5.9914646,5.0,6.73,1,0,0,0.1778,0,0,1,0,0.0,1,1,400.0,5.9914646,400.0,5.9914646,400.0,5.9914646,5.521461,5.521461,1,0.94050002,1,250.0,5.521461,250.0,5.521461
+Nigeria,NGA,7.6029005,5.5500002,6.8099999,1,1,0,0.1111,0,0,1,0,0.0,1,1,2004.0,7.6029005,2004.0,7.6029005,2004.0,7.6029005,5.521461,5.521461,1,0.94999999,0,250.0,5.521461,250.0,5.521461
+Nicaragua,NIC,5.0955892,5.23,7.54,1,0,0,0.1444,0,0,0,0,20.0,1,1,,,93.25,4.535284,130.0,4.8675346,5.0955892,4.8675346,0,0.044,0,93.25,4.535284,,
+New Zealand,NZL,2.1459312,9.7299995,9.7600002,0,1,0,0.45559999,1,0,0,1,91.699997,1,1,8.5500002,2.1459312,8.5500002,2.1459312,8.5500002,2.1459312,2.1459312,2.1459312,0,0.0,0,8.5500002,2.1459312,8.5500002,2.1459312
+Pakistan,PAK,3.6106477,6.0500002,7.3499999,1,0,0,0.33329999,0,1,0,0,0.0,1,1,36.990002,3.6106477,36.990002,3.6106477,36.990002,3.6106477,3.6106477,3.6106477,0,0.53757,0,36.990002,3.6106477,36.990002,3.6106477
+Panama,PAN,5.0955892,5.9099998,8.8400002,1,0,0,0.1,0,0,0,0,20.0,1,1,15.07,2.7127061,30.5,3.4177268,130.0,4.8675346,5.0955892,4.8675346,0,0.08004,0,30.5,3.4177268,15.07,2.7127061
+Peru,PER,4.2626801,5.77,8.3999996,1,0,0,0.1111,0,0,0,0,30.000002,1,1,15.07,2.7127061,30.5,3.4177268,56.5,4.0342407,4.2626801,4.0342407,0,0.00050000002,0,30.5,3.4177268,15.07,2.7127061
+Paraguay,PRY,4.3579903,6.9499998,8.21,1,0,0,0.25560001,0,0,0,0,25.0,1,1,,,93.25,4.535284,62.200001,4.1303549,4.3579903,4.1303549,0,0.0,0,93.25,4.535284,,
+Sudan,SDN,4.4796071,4.0,7.3099999,1,1,0,0.16670001,0,0,1,0,0.0,1,1,88.199997,4.4796071,88.199997,4.4796071,88.199997,4.4796071,4.4796071,4.4796071,1,0.93099999,0,88.199997,4.4796071,88.199997,4.4796071
+Senegal,SEN,5.1038828,6.0,7.4000001,0,1,0,0.1556,0,0,1,0,0.0,0,1,164.66,5.1038828,164.66,5.1038828,164.66,5.1038828,5.1038828,5.1038828,1,0.94999999,0,164.66,5.1038828,164.66,5.1038828
+Singapore,SGP,2.8735647,9.3199997,10.15,0,0,0,0.0136,0,1,0,0,0.0,0,1,17.700001,2.8735647,17.700001,2.8735647,17.700001,2.8735647,2.8735647,2.8735647,0,0.0,0,17.700001,2.8735647,17.700001,2.8735647
+Sierra Leone,SLE,6.1800165,5.8200002,6.25,1,1,0,0.092200004,0,0,1,0,0.0,1,1,483.0,6.1800165,483.0,6.1800165,483.0,6.1800165,5.521461,5.521461,1,0.94999999,0,250.0,5.521461,250.0,5.521461
+El Salvador,SLV,4.3579903,5.0,7.9499998,1,0,0,0.15000001,0,0,0,0,20.0,1,1,,,93.25,4.535284,62.200001,4.1303549,4.3579903,4.1303549,0,0.0,0,93.25,4.535284,,
+Togo,TGO,6.5042882,6.9099998,7.2199998,1,0,0,0.0889,0,0,1,0,0.0,1,1,668.0,6.5042882,668.0,6.5042882,668.0,6.5042882,5.521461,5.521461,1,0.94999999,1,250.0,5.521461,250.0,5.521461
+Trinidad and Tobago,TTO,4.4426513,7.4499998,8.7700005,0,1,0,0.1222,0,0,0,0,40.0,0,1,85.0,4.4426513,85.0,4.4426513,85.0,4.4426513,4.4426513,4.4426513,0,0.0,0,85.0,4.4426513,85.0,4.4426513
+Tunisia,TUN,4.1431346,6.4499998,8.4799995,1,1,0,0.37779999,0,0,1,0,0.0,1,1,63.0,4.1431346,63.0,4.1431346,63.0,4.1431346,4.1431346,4.1431346,0,0.0,0,63.0,4.1431346,63.0,4.1431346
+Tanzania,TZA,4.9767337,6.6399999,6.25,0,0,1,0.066699997,0,0,1,0,0.0,0,0,145.0,4.9767337,145.0,4.9767337,145.0,4.9767337,4.9767337,4.9767337,1,0.92150003,1,145.0,4.9767337,145.0,4.9767337
+Uganda,UGA,5.6347895,4.4499998,6.9699998,1,0,0,0.0111,0,0,1,0,0.0,1,1,280.0,5.6347895,280.0,5.6347895,280.0,5.6347895,5.521461,5.521461,1,0.94999999,1,250.0,5.521461,250.0,5.521461
+Uruguary,URY,4.2626801,7.0,9.0299997,1,0,0,0.36669999,0,0,0,0,90.0,1,1,,,93.25,4.535284,56.5,4.0342407,4.2626801,4.0342407,0,0.0,0,93.25,4.535284,,
+USA,USA,2.7080503,10.0,10.22,0,1,0,0.42219999,1,0,0,0,83.600006,0,1,15.0,2.7080503,15.0,2.7080503,15.0,2.7080503,2.7080503,2.7080503,0,0.0,0,15.0,2.7080503,15.0,2.7080503
+Venezuela,VEN,4.3579903,7.1399999,9.0699997,1,0,0,0.0889,0,0,0,0,20.0,1,1,,,93.25,4.535284,62.200001,4.1303549,4.3579903,4.1303549,0,0.0070400001,0,93.25,4.535284,,
+Vietnam,VNM,4.9416423,6.4099998,7.2800002,1,1,0,0.1778,0,1,0,0,0.0,1,1,140.0,4.9416423,140.0,4.9416423,140.0,4.9416423,4.9416423,4.9416423,0,0.70109999,0,140.0,4.9416423,140.0,4.9416423
+South Africa,ZAF,2.74084,6.8600001,8.8900003,0,1,0,0.3222,0,0,1,0,16.0,0,1,15.5,2.74084,15.5,2.74084,15.5,2.74084,2.74084,2.74084,0,0.1045,0,15.5,2.74084,15.5,2.74084
+Zaire,ZAR,5.480639,3.5,6.8699999,0,0,1,0.0,0,0,1,0,0.0,0,0,240.0,5.480639,240.0,5.480639,240.0,5.480639,5.480639,5.480639,1,0.94999999,1,240.0,5.480639,240.0,5.480639

causalpy/data/datasets.py

@@ -16,6 +16,7 @@
     "sc": {"filename": "synthetic_control.csv"},
     "anova1": {"filename": "ancova_generated.csv"},
     "geolift1": {"filename": "geolift1.csv"},
+    "risk": {"filename": "AJR2001.csv"},
 }
 
 

causalpy/pymc_experiments.py

@@ -8,6 +8,7 @@
 import seaborn as sns
 import xarray as xr
 from patsy import build_design_matrices, dmatrices
+from sklearn.linear_model import LinearRegression as sk_lin_reg
 
 from causalpy.custom_exceptions import BadIndexException  # NOQA
 from causalpy.custom_exceptions import DataException, FormulaException
@@ -883,3 +884,130 @@ def _get_treatment_effect_coeff(self) -> str:
                 return label
 
         raise NameError("Unable to find coefficient name for the treatment effect")
+
+
+class InstrumentalVariable(ExperimentalDesign):
+    """
+    A class to analyse instrumental variable style experiments.
+
+    :param instruments_data: A pandas dataframe of instruments
+                             for our treatment variable. Should contain
+                             instruments Z, and treatment t
+    :param data: A pandas dataframe of covariates for fitting
+                 the focal regression of interest. Should contain covariates X
+                 including treatment t and outcome y
+    :param instruments_formula: A statistical model formula for
+                                the instrumental stage regression
+                                e.g. t ~ 1 + z1 + z2 + z3
+    :param formula: A statistical model formula for the \n
+                    focal regression e.g. y ~ 1 + t + x1 + x2 + x3
+    :param model: A PyMC model
+    :param priors: An optional dictionary of priors for the
+                   mus and sigmas of both regressions. If priors are not
+                   specified we will substitue MLE estimates for the beta
+                   coefficients. Greater control can be achieved
+                   by specifying the priors directly e.g. priors = {
+                                    "mus": [0, 0],
+                                    "sigmas": [1, 1],
+                                    "eta": 2,
+                                    "lkj_sd": 2,
+                                    }
+
+    """
+
+    def __init__(
+        self,
+        instruments_data: pd.DataFrame,
+        data: pd.DataFrame,
+        instruments_formula: str,
+        formula: str,
+        model=None,
+        priors=None,
+        **kwargs,
+    ):
+        super().__init__(model=model, **kwargs)
+        self.expt_type = "Instrumental Variable Regression"
+        self.data = data
+        self.instruments_data = instruments_data
+        self.formula = formula
+        self.instruments_formula = instruments_formula
+        self.model = model
+        self._input_validation()
+
+        y, X = dmatrices(formula, self.data)
+        self._y_design_info = y.design_info
+        self._x_design_info = X.design_info
+        self.labels = X.design_info.column_names
+        self.y, self.X = np.asarray(y), np.asarray(X)
+        self.outcome_variable_name = y.design_info.column_names[0]
+
+        t, Z = dmatrices(instruments_formula, self.instruments_data)
+        self._t_design_info = t.design_info
+        self._z_design_info = Z.design_info
+        self.labels_instruments = Z.design_info.column_names
+        self.t, self.Z = np.asarray(t), np.asarray(Z)
+        self.instrument_variable_name = t.design_info.column_names[0]
+
+        self.get_naive_OLS_fit()
+        self.get_2SLS_fit()
+
+        # fit the model to the data
+        COORDS = {"instruments": self.labels_instruments, "covariates": self.labels}
+        self.coords = COORDS
+        if priors is None:
+            priors = {
+                "mus": [self.ols_beta_first_params, self.ols_beta_second_params],
+                "sigmas": [1, 1],
+                "eta": 2,
+                "lkj_sd": 2,
+            }
+        self.priors = priors
+        self.model.fit(
+            X=self.X, Z=self.Z, y=self.y, t=self.t, coords=COORDS, priors=self.priors
+        )
+
+    def get_2SLS_fit(self):
+        first_stage_reg = sk_lin_reg().fit(self.Z, self.t)
+        fitted_Z_values = first_stage_reg.predict(self.Z)
+        X2 = self.data.copy(deep=True)
+        X2[self.instrument_variable_name] = fitted_Z_values
+        _, X2 = dmatrices(self.formula, X2)
+        second_stage_reg = sk_lin_reg().fit(X=X2, y=self.y)
+        betas_first = list(first_stage_reg.coef_[0][1:])
+        betas_first.insert(0, first_stage_reg.intercept_[0])
+        betas_second = list(second_stage_reg.coef_[0][1:])
+        betas_second.insert(0, second_stage_reg.intercept_[0])
+        self.ols_beta_first_params = betas_first
+        self.ols_beta_second_params = betas_second
+        self.first_stage_reg = first_stage_reg
+        self.second_stage_reg = second_stage_reg
+
+    def get_naive_OLS_fit(self):
+        ols_reg = sk_lin_reg().fit(self.X, self.y)
+        beta_params = list(ols_reg.coef_[0][1:])
+        beta_params.insert(0, ols_reg.intercept_[0])
+        self.ols_beta_params = dict(zip(self._x_design_info.column_names, beta_params))
+        self.ols_reg = ols_reg
+
+    def _input_validation(self):
+        """Validate the input data and model formula for correctness"""
+        treatment = self.instruments_formula.split("~")[0]
+        test = treatment.strip() in self.instruments_data.columns
+        test = test & (treatment.strip() in self.data.columns)
+        if not test:
+            raise DataException(
+                f"""
+                The treatment variable:
+                {treatment} must appear in the instrument_data to be used
+                as an outcome variable and in the data object to be used as a covariate.
+                """
+            )
+        Z = self.data[treatment.strip()]
+        check_binary = len(np.unique(Z)) > 2
+        if check_binary:
+            warnings.warn(
+                """Warning. The treatment variable is not Binary.
+                This is not necessarily a problem but it violates
+                the assumption of a simple IV experiment.
+                The coefficients should be interpreted appropriately."""
+            )