{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2020:X7EHOSWXVUAHRBS4TNCVQ5XGYT","short_pith_number":"pith:X7EHOSWX","canonical_record":{"source":{"id":"2010.01800","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"econ.EM","submitted_at":"2020-10-05T06:21:02Z","cross_cats_sorted":[],"title_canon_sha256":"55a3b1eb558af88f8e7d0d0b9430994c2f245920839ab586e6de413e937f24dc","abstract_canon_sha256":"86f20fb18e3e966ae3fab74308ab380b99e27215a222b98b15d69eb2b5ef1f3f"},"schema_version":"1.0"},"canonical_sha256":"bfc8774ad7ad0078865c9b455876e6c4c12a6af3c4d59f6c437db97e08e0ae13","source":{"kind":"arxiv","id":"2010.01800","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2010.01800","created_at":"2026-07-05T09:59:10Z"},{"alias_kind":"arxiv_version","alias_value":"2010.01800v2","created_at":"2026-07-05T09:59:10Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2010.01800","created_at":"2026-07-05T09:59:10Z"},{"alias_kind":"pith_short_12","alias_value":"X7EHOSWXVUAH","created_at":"2026-07-05T09:59:10Z"},{"alias_kind":"pith_short_16","alias_value":"X7EHOSWXVUAHRBS4","created_at":"2026-07-05T09:59:10Z"},{"alias_kind":"pith_short_8","alias_value":"X7EHOSWX","created_at":"2026-07-05T09:59:10Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2020:X7EHOSWXVUAHRBS4TNCVQ5XGYT","target":"record","payload":{"canonical_record":{"source":{"id":"2010.01800","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"econ.EM","submitted_at":"2020-10-05T06:21:02Z","cross_cats_sorted":[],"title_canon_sha256":"55a3b1eb558af88f8e7d0d0b9430994c2f245920839ab586e6de413e937f24dc","abstract_canon_sha256":"86f20fb18e3e966ae3fab74308ab380b99e27215a222b98b15d69eb2b5ef1f3f"},"schema_version":"1.0"},"canonical_sha256":"bfc8774ad7ad0078865c9b455876e6c4c12a6af3c4d59f6c437db97e08e0ae13","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:59:10.460040Z","signature_b64":"SBRZGr8q1vhBqocCw9UBE+yq+11IrJO7eGRiuCkn68mquU4Ge0i3+zPlq4OlGjkcrGQ7RcaZd+pMNYKh+46RBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"bfc8774ad7ad0078865c9b455876e6c4c12a6af3c4d59f6c437db97e08e0ae13","last_reissued_at":"2026-07-05T09:59:10.459578Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:59:10.459578Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2010.01800","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T09:59:10Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"R+ec4xUurwbchdE3SFOFEZTNQuCpW0i5fI80ISOURMzarnwTkMlXwqkgtTDhPzRwV6HgKr6entzzc6wjFUEKAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-18T12:31:33.074827Z"},"content_sha256":"88ea4233ebd13741a644daa8d28b57f7491e7c68caa508279a58251bb79aacfc","schema_version":"1.0","event_id":"sha256:88ea4233ebd13741a644daa8d28b57f7491e7c68caa508279a58251bb79aacfc"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2020:X7EHOSWXVUAHRBS4TNCVQ5XGYT","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Robust and Efficient Estimation of Potential Outcome Means under Random Assignment","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"econ.EM","authors_text":"Akanksha Negi, Jeffrey M. Wooldridge","submitted_at":"2020-10-05T06:21:02Z","abstract_excerpt":"We study efficiency improvements in randomized experiments for estimating a vector of potential outcome means using regression adjustment (RA) when there are more than two treatment levels. We show that linear RA which estimates separate slopes for each assignment level is never worse, asymptotically, than using the subsample averages. We also show that separate RA improves over pooled RA except in the obvious case where slope parameters in the linear projections are identical across the different assignment levels. We further characterize the class of nonlinear RA methods that preserve consis"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2010.01800","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2010.01800/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T09:59:10Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"t427zuAHF5yH8dtNgDDNAgnWXj98wX2Y+Fi2aM9Zag+hsCmdC32cT6uFsroe1HWbgYzwh5KBZvMXkj+6E69uAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-18T12:31:33.075373Z"},"content_sha256":"04076fb492273c3152cd9e493bfa8721583e748104111eaab731cfd4acbaa052","schema_version":"1.0","event_id":"sha256:04076fb492273c3152cd9e493bfa8721583e748104111eaab731cfd4acbaa052"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/X7EHOSWXVUAHRBS4TNCVQ5XGYT/bundle.json","state_url":"https://pith.science/pith/X7EHOSWXVUAHRBS4TNCVQ5XGYT/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/X7EHOSWXVUAHRBS4TNCVQ5XGYT/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-18T12:31:33Z","links":{"resolver":"https://pith.science/pith/X7EHOSWXVUAHRBS4TNCVQ5XGYT","bundle":"https://pith.science/pith/X7EHOSWXVUAHRBS4TNCVQ5XGYT/bundle.json","state":"https://pith.science/pith/X7EHOSWXVUAHRBS4TNCVQ5XGYT/state.json","well_known_bundle":"https://pith.science/.well-known/pith/X7EHOSWXVUAHRBS4TNCVQ5XGYT/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:X7EHOSWXVUAHRBS4TNCVQ5XGYT","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"86f20fb18e3e966ae3fab74308ab380b99e27215a222b98b15d69eb2b5ef1f3f","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"econ.EM","submitted_at":"2020-10-05T06:21:02Z","title_canon_sha256":"55a3b1eb558af88f8e7d0d0b9430994c2f245920839ab586e6de413e937f24dc"},"schema_version":"1.0","source":{"id":"2010.01800","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2010.01800","created_at":"2026-07-05T09:59:10Z"},{"alias_kind":"arxiv_version","alias_value":"2010.01800v2","created_at":"2026-07-05T09:59:10Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2010.01800","created_at":"2026-07-05T09:59:10Z"},{"alias_kind":"pith_short_12","alias_value":"X7EHOSWXVUAH","created_at":"2026-07-05T09:59:10Z"},{"alias_kind":"pith_short_16","alias_value":"X7EHOSWXVUAHRBS4","created_at":"2026-07-05T09:59:10Z"},{"alias_kind":"pith_short_8","alias_value":"X7EHOSWX","created_at":"2026-07-05T09:59:10Z"}],"graph_snapshots":[{"event_id":"sha256:04076fb492273c3152cd9e493bfa8721583e748104111eaab731cfd4acbaa052","target":"graph","created_at":"2026-07-05T09:59:10Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2010.01800/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We study efficiency improvements in randomized experiments for estimating a vector of potential outcome means using regression adjustment (RA) when there are more than two treatment levels. We show that linear RA which estimates separate slopes for each assignment level is never worse, asymptotically, than using the subsample averages. We also show that separate RA improves over pooled RA except in the obvious case where slope parameters in the linear projections are identical across the different assignment levels. We further characterize the class of nonlinear RA methods that preserve consis","authors_text":"Akanksha Negi, Jeffrey M. Wooldridge","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"econ.EM","submitted_at":"2020-10-05T06:21:02Z","title":"Robust and Efficient Estimation of Potential Outcome Means under Random Assignment"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2010.01800","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:88ea4233ebd13741a644daa8d28b57f7491e7c68caa508279a58251bb79aacfc","target":"record","created_at":"2026-07-05T09:59:10Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"86f20fb18e3e966ae3fab74308ab380b99e27215a222b98b15d69eb2b5ef1f3f","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"econ.EM","submitted_at":"2020-10-05T06:21:02Z","title_canon_sha256":"55a3b1eb558af88f8e7d0d0b9430994c2f245920839ab586e6de413e937f24dc"},"schema_version":"1.0","source":{"id":"2010.01800","kind":"arxiv","version":2}},"canonical_sha256":"bfc8774ad7ad0078865c9b455876e6c4c12a6af3c4d59f6c437db97e08e0ae13","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"bfc8774ad7ad0078865c9b455876e6c4c12a6af3c4d59f6c437db97e08e0ae13","first_computed_at":"2026-07-05T09:59:10.459578Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:59:10.459578Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"SBRZGr8q1vhBqocCw9UBE+yq+11IrJO7eGRiuCkn68mquU4Ge0i3+zPlq4OlGjkcrGQ7RcaZd+pMNYKh+46RBg==","signature_status":"signed_v1","signed_at":"2026-07-05T09:59:10.460040Z","signed_message":"canonical_sha256_bytes"},"source_id":"2010.01800","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:88ea4233ebd13741a644daa8d28b57f7491e7c68caa508279a58251bb79aacfc","sha256:04076fb492273c3152cd9e493bfa8721583e748104111eaab731cfd4acbaa052"],"state_sha256":"f56dcbb2e562505dad7231e6a0baeec09a64363c600eb089d562e789e4c4a2cb"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"NFUcaMQlDSsbLEMkpsTMxb3FFmVMzFrCoYYRfW8ACign+sRwMRW38SjSXwD3YhIVICO3OhUCN5643OURRxFiCA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-18T12:31:33.080745Z","bundle_sha256":"6e42fa3dc7c1a3dcca867ed335ccf33083d9e1e32be95441915bd4ba058a89ca"}}