diff --git a/azcausal/estimators/panel/vdid.py b/azcausal/estimators/panel/vdid.py
index 68c2418..075925f 100644
--- a/azcausal/estimators/panel/vdid.py
+++ b/azcausal/estimators/panel/vdid.py
@@ -82,19 +82,17 @@ def group_by_index(dx):
     return dx.groupby(list(dx.index.names))
 
 
-def dot_by_columns(ds, columns, name, weight=None):
+def dot_by_columns(ds, dim, name, weight=None):
     if weight is None:
         weight = dict()
-    counts = columns.map(len)
+    counts = dim.map(len)
 
     avg = dict()
-    for k, v in columns.items():
+    for k, v in dim.items():
         v = [e for e in v if e in ds.columns]
 
-        if k in weight:
+        if weight.get(k, None) is not None:
             w = np.array([weight[k].get(e, 0.0) for e in v])
-            w = (w / np.abs(w).sum()) * np.abs(weight[k]).sum()
-
             avg[k] = ds[v].values @ w
         else:
             avg[k] = np.sum(ds[v], axis=1) / counts[k]
@@ -110,7 +108,7 @@ def vdid_avg_by(dx, label, col, dim=None, weight=None):
     if weight is None:
         avg = group_by_index(dx.droplevel(col, axis='index')).sum().divide(counts, axis='index', level=label)
     else:
-        avg = dot_by_columns(dx.droplevel(label, axis='index').unstack(col), dim, label, weight=weight).stack()
+        avg = dot_by_columns(dx.droplevel(label, axis='index').unstack(col).fillna(0.0), dim, label, weight=weight).stack()
 
     return avg, counts
 
@@ -138,17 +136,34 @@ def vdid_take_by_column(dx, col):
     return dx
 
 
+def vdid_fix_weights(treatment, weights):
+    ans = dict()
+    for k, x in treatment.items():
+        weight = weights.get(k, None)
+
+        if weight is not None:
+            x = list(set(x))
+            weight = weight.loc[x]
+            ans[k] = weight / weight.sum()
+
+    return pd.Series(ans)
+
+
 def vdid_jackknife():
-    def sample(treatment: pd.Series) -> Iterator[pd.Series]:
+    def sample(treatment: pd.Series, weights: pd.Series) -> Iterator[pd.Series]:
         treat, contr = treatment[True], treatment[False]
 
         if len(contr) > 1:
             for i in range(len(contr)):
-                yield pd.Series({True: treat, False: np.delete(contr, i)})
+                treatment_mod = pd.Series({True: treat, False: np.delete(contr, i)})
+                weight_mod = vdid_fix_weights(treatment_mod, weights)
+                yield treatment_mod, weight_mod
 
         if len(treat) > 1:
             for i in range(len(treat)):
-                yield pd.Series({True: np.delete(treat, i), False: contr})
+                treatment_mod = pd.Series({True: np.delete(treat, i), False: contr})
+                weight_mod = vdid_fix_weights(treatment_mod, weights)
+                yield treatment_mod, weight_mod
 
     def fit(dse: pd.DataFrame):
         n = len(dse)
@@ -172,7 +187,7 @@ def sample(treatment: pd.Series) -> Iterator[pd.Series]:
 
 
 def vdid_bootstrap(n_samples=1000, seed=1):
-    def sample(treatment: pd.Series) -> Iterator[pd.Series]:
+    def sample(treatment: pd.Series, weights: pd.Series) -> Iterator[pd.Series]:
 
         H = dict()
         for unit in treatment[True]:
@@ -193,7 +208,7 @@ def sample(treatment: pd.Series) -> Iterator[pd.Series]:
 
             # if we have at least one treatment and one control
             if tt.map(lambda x: len(x) > 0).all():
-                yield tt
+                yield tt, vdid_fix_weights(tt, weights)
                 cnt += 1
 
     return sample, vdid_se
@@ -281,8 +296,8 @@ def vdid(dx: pd.DataFrame,
         ci_sample, ci_fit = ci
 
         # simulate based on the standard error method
-        ci_samp_dict = {sample: f(vdid_ratio(dot_by_columns(matrix, treatment_mod, randomize, weight=weight).stack(), ratio))
-                        for sample, treatment_mod in enumerate(ci_sample(units))}
+        ci_samp_dict = {sample: f(vdid_ratio(dot_by_columns(matrix, treatment_mod, randomize, weight=weight_mod).stack(), ratio))
+                        for sample, (treatment_mod, weight_mod) in enumerate(ci_sample(units, weight))}
 
         ci_samp = pd.DataFrame(ci_samp_dict).rename_axis('sample', axis=1).stack()