{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:2V2U3JUHLQ64ZFHTA6G7374BRG","short_pith_number":"pith:2V2U3JUH","canonical_record":{"source":{"id":"1803.07164","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"econ.EM","submitted_at":"2018-03-19T21:02:51Z","cross_cats_sorted":["cs.GT","cs.LG","math.ST","stat.ML","stat.TH"],"title_canon_sha256":"9f0248087f38028e1b743b7e5552bd59fcee3863ebf5da7531f28ed93ae479b3","abstract_canon_sha256":"f470a474c028b763d719d780c916ffde73e58cff89fd1322ab1f8d17df64fe35"},"schema_version":"1.0"},"canonical_sha256":"d5754da6875c3dcc94f3078dfdff8189a0e4320060075730d642260585d15bbd","source":{"kind":"arxiv","id":"1803.07164","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1803.07164","created_at":"2026-05-18T00:17:38Z"},{"alias_kind":"arxiv_version","alias_value":"1803.07164v2","created_at":"2026-05-18T00:17:38Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1803.07164","created_at":"2026-05-18T00:17:38Z"},{"alias_kind":"pith_short_12","alias_value":"2V2U3JUHLQ64","created_at":"2026-05-18T12:32:02Z"},{"alias_kind":"pith_short_16","alias_value":"2V2U3JUHLQ64ZFHT","created_at":"2026-05-18T12:32:02Z"},{"alias_kind":"pith_short_8","alias_value":"2V2U3JUH","created_at":"2026-05-18T12:32:02Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:2V2U3JUHLQ64ZFHTA6G7374BRG","target":"record","payload":{"canonical_record":{"source":{"id":"1803.07164","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"econ.EM","submitted_at":"2018-03-19T21:02:51Z","cross_cats_sorted":["cs.GT","cs.LG","math.ST","stat.ML","stat.TH"],"title_canon_sha256":"9f0248087f38028e1b743b7e5552bd59fcee3863ebf5da7531f28ed93ae479b3","abstract_canon_sha256":"f470a474c028b763d719d780c916ffde73e58cff89fd1322ab1f8d17df64fe35"},"schema_version":"1.0"},"canonical_sha256":"d5754da6875c3dcc94f3078dfdff8189a0e4320060075730d642260585d15bbd","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:17:38.220935Z","signature_b64":"2goCWzCmJy/fB7IBpydx25+XstHfwnZGtbd05ultEc00n7D7EN7cT1MP5eycdU8+XwYa3Y1d+UokBGggQa4HBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d5754da6875c3dcc94f3078dfdff8189a0e4320060075730d642260585d15bbd","last_reissued_at":"2026-05-18T00:17:38.220441Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:17:38.220441Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1803.07164","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":"2026-05-18T00:17:38Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"JZRPB0NkBo7+qLA38bg8IFZvLl/ugDm3wAewDUoNspZSK8oatgtphGPYEIq4eIA4qqVYpbIcty/va3iqSX77Bw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-04T11:18:19.797654Z"},"content_sha256":"fbe5e72515a8913b06b221eb0cb4f6d975d68df2440d1a08b0cb4d2dafc5bda0","schema_version":"1.0","event_id":"sha256:fbe5e72515a8913b06b221eb0cb4f6d975d68df2440d1a08b0cb4d2dafc5bda0"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:2V2U3JUHLQ64ZFHTA6G7374BRG","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Adversarial Generalized Method of Moments","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.GT","cs.LG","math.ST","stat.ML","stat.TH"],"primary_cat":"econ.EM","authors_text":"Greg Lewis, Vasilis Syrgkanis","submitted_at":"2018-03-19T21:02:51Z","abstract_excerpt":"We provide an approach for learning deep neural net representations of models described via conditional moment restrictions. Conditional moment restrictions are widely used, as they are the language by which social scientists describe the assumptions they make to enable causal inference. We formulate the problem of estimating the underling model as a zero-sum game between a modeler and an adversary and apply adversarial training. Our approach is similar in nature to Generative Adversarial Networks (GAN), though here the modeler is learning a representation of a function that satisfies a contin"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1803.07164","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":""},"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":"2026-05-18T00:17:38Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"/5tIMgyGx/EFZkiZ+xKWv6dpBGRIX2LE6SYJpwOT9nTKAEzWTHmVpzmCeB9iNhQ1lB9LChatfwM2h0IY/HygCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-04T11:18:19.798008Z"},"content_sha256":"7ed6add13d9a9952688feeeaa5ca591032d21adbf762316627d31414ba6d2a70","schema_version":"1.0","event_id":"sha256:7ed6add13d9a9952688feeeaa5ca591032d21adbf762316627d31414ba6d2a70"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/2V2U3JUHLQ64ZFHTA6G7374BRG/bundle.json","state_url":"https://pith.science/pith/2V2U3JUHLQ64ZFHTA6G7374BRG/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/2V2U3JUHLQ64ZFHTA6G7374BRG/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":"2026-06-04T11:18:19Z","links":{"resolver":"https://pith.science/pith/2V2U3JUHLQ64ZFHTA6G7374BRG","bundle":"https://pith.science/pith/2V2U3JUHLQ64ZFHTA6G7374BRG/bundle.json","state":"https://pith.science/pith/2V2U3JUHLQ64ZFHTA6G7374BRG/state.json","well_known_bundle":"https://pith.science/.well-known/pith/2V2U3JUHLQ64ZFHTA6G7374BRG/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:2V2U3JUHLQ64ZFHTA6G7374BRG","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":"f470a474c028b763d719d780c916ffde73e58cff89fd1322ab1f8d17df64fe35","cross_cats_sorted":["cs.GT","cs.LG","math.ST","stat.ML","stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"econ.EM","submitted_at":"2018-03-19T21:02:51Z","title_canon_sha256":"9f0248087f38028e1b743b7e5552bd59fcee3863ebf5da7531f28ed93ae479b3"},"schema_version":"1.0","source":{"id":"1803.07164","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1803.07164","created_at":"2026-05-18T00:17:38Z"},{"alias_kind":"arxiv_version","alias_value":"1803.07164v2","created_at":"2026-05-18T00:17:38Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1803.07164","created_at":"2026-05-18T00:17:38Z"},{"alias_kind":"pith_short_12","alias_value":"2V2U3JUHLQ64","created_at":"2026-05-18T12:32:02Z"},{"alias_kind":"pith_short_16","alias_value":"2V2U3JUHLQ64ZFHT","created_at":"2026-05-18T12:32:02Z"},{"alias_kind":"pith_short_8","alias_value":"2V2U3JUH","created_at":"2026-05-18T12:32:02Z"}],"graph_snapshots":[{"event_id":"sha256:7ed6add13d9a9952688feeeaa5ca591032d21adbf762316627d31414ba6d2a70","target":"graph","created_at":"2026-05-18T00:17:38Z","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"},"paper":{"abstract_excerpt":"We provide an approach for learning deep neural net representations of models described via conditional moment restrictions. Conditional moment restrictions are widely used, as they are the language by which social scientists describe the assumptions they make to enable causal inference. We formulate the problem of estimating the underling model as a zero-sum game between a modeler and an adversary and apply adversarial training. Our approach is similar in nature to Generative Adversarial Networks (GAN), though here the modeler is learning a representation of a function that satisfies a contin","authors_text":"Greg Lewis, Vasilis Syrgkanis","cross_cats":["cs.GT","cs.LG","math.ST","stat.ML","stat.TH"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"econ.EM","submitted_at":"2018-03-19T21:02:51Z","title":"Adversarial Generalized Method of Moments"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1803.07164","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:fbe5e72515a8913b06b221eb0cb4f6d975d68df2440d1a08b0cb4d2dafc5bda0","target":"record","created_at":"2026-05-18T00:17:38Z","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":"f470a474c028b763d719d780c916ffde73e58cff89fd1322ab1f8d17df64fe35","cross_cats_sorted":["cs.GT","cs.LG","math.ST","stat.ML","stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"econ.EM","submitted_at":"2018-03-19T21:02:51Z","title_canon_sha256":"9f0248087f38028e1b743b7e5552bd59fcee3863ebf5da7531f28ed93ae479b3"},"schema_version":"1.0","source":{"id":"1803.07164","kind":"arxiv","version":2}},"canonical_sha256":"d5754da6875c3dcc94f3078dfdff8189a0e4320060075730d642260585d15bbd","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"d5754da6875c3dcc94f3078dfdff8189a0e4320060075730d642260585d15bbd","first_computed_at":"2026-05-18T00:17:38.220441Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:17:38.220441Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"2goCWzCmJy/fB7IBpydx25+XstHfwnZGtbd05ultEc00n7D7EN7cT1MP5eycdU8+XwYa3Y1d+UokBGggQa4HBg==","signature_status":"signed_v1","signed_at":"2026-05-18T00:17:38.220935Z","signed_message":"canonical_sha256_bytes"},"source_id":"1803.07164","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:fbe5e72515a8913b06b221eb0cb4f6d975d68df2440d1a08b0cb4d2dafc5bda0","sha256:7ed6add13d9a9952688feeeaa5ca591032d21adbf762316627d31414ba6d2a70"],"state_sha256":"6bd7db0d7be4d1e88d106975494b765013a7791df95971138deb66f179948d70"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"8j2eZQA5RljzJ2Ur+CfgXzoyS9bMwbNN0FfqOCj8gULC5UWDIHcCX4Q3VqvT0M2/z7AfRwkWxCvaOBUEJ/A2BA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-04T11:18:19.799998Z","bundle_sha256":"427043e6cf23f6c9fa58f788021dd346400b4ffe59e08f6b92b4cd05946fffbb"}}