Save all geography variables to CD-stacked datasets

Updates stacked_dataset_builder.py to:
- Set all geography variables from block assignment (block_geoid, tract_geoid,
  cbsa_code, sldu, sldl, place_fips, vtd, puma)
- Include these variables in the saved h5 files

These variables enable granular geographic analysis at multiple levels:
state legislative districts, census tracts, metro areas, cities, etc.

Requires policyengine-us#7249 for the variable definitions.

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

Author: MaxGhenis

changelog_entry.yaml

@@ -1,7 +1,7 @@
 - bump: minor
   changes:
     added:
-    - Census block-level geographic assignment for households
-    - Comprehensive geography lookups from block GEOID (SLDU, SLDL, Place, VTD, PUMA)
+    - Census block-level geographic assignment for households in CD-stacked datasets
+    - Comprehensive geography variables in output (block_geoid, tract_geoid, cbsa_code, sldu, sldl, place_fips, vtd, puma)
     - Block crosswalk file mapping 8.1M blocks to all Census geographies
     - Block-to-CD distribution file for population-weighted assignment

policyengine_us_data/datasets/cps/local_area_calibration/stacked_dataset_builder.py

@@ -362,6 +362,16 @@ def create_sparse_cd_stacked_dataset(
         # Set county using indices for backwards compatibility with PolicyEngine-US
         cd_sim.set_input("county", time_period, geography["county_index"])
 
+        # Set all other geography variables from block assignment
+        cd_sim.set_input("block_geoid", time_period, geography["block_geoid"])
+        cd_sim.set_input("tract_geoid", time_period, geography["tract_geoid"])
+        cd_sim.set_input("cbsa_code", time_period, geography["cbsa_code"])
+        cd_sim.set_input("sldu", time_period, geography["sldu"])
+        cd_sim.set_input("sldl", time_period, geography["sldl"])
+        cd_sim.set_input("place_fips", time_period, geography["place_fips"])
+        cd_sim.set_input("vtd", time_period, geography["vtd"])
+        cd_sim.set_input("puma", time_period, geography["puma"])
+
         # Note: We no longer use binary filtering for county_filter.
         # Instead, weights are scaled by P(target|CD) and all households
         # are included to avoid sample selection bias.
@@ -636,6 +646,16 @@ def create_sparse_cd_stacked_dataset(
     # spm_unit_spm_threshold is recalculated with CD-specific geo-adjustment
     vars_to_save.add("spm_unit_spm_threshold")
 
+    # Add all geography variables set during block assignment
+    vars_to_save.add("block_geoid")
+    vars_to_save.add("tract_geoid")
+    vars_to_save.add("cbsa_code")
+    vars_to_save.add("sldu")
+    vars_to_save.add("sldl")
+    vars_to_save.add("place_fips")
+    vars_to_save.add("vtd")
+    vars_to_save.add("puma")
+
     variables_saved = 0
     variables_skipped = 0