{ "_id": "6a1ed38bb401979e7340e034", "Package": "causalweight", "Title": "Estimation Methods for Causal Inference Based on Inverse\nProbability Weighting and Doubly Robust Estimation", "Version": "1.1.4", "Authors@R": "c(person(given = \"Hugo\",\nfamily = \"Bodory\",\nrole = c(\"aut\", \"cre\"),\nemail = \"hugo.bodory@unisg.ch\",\ncomment = c(ORCID = \"0000-0002-3645-1204\")),\nperson(given = \"Martin\",\nfamily = \"Huber\",\nrole = \"aut\",\nemail = \"martin.huber@unifr.ch\",\ncomment = c(ORCID = \"0000-0002-8590-9402\")),\nperson(given = \"Jannis\",\nfamily = \"Kueck\",\nrole = \"aut\",\nemail = \"jannis.kueck@uni-hamburg.de\",\ncomment = c(ORCID = \"0000-0003-4367-0285\")))", "Maintainer": "Hugo Bodory ", "Description": "Various estimators of causal effects based on inverse\nprobability weighting, doubly robust estimation, and double\nmachine learning. Specifically, the package includes methods\nfor estimating average treatment effects, direct and indirect\neffects in causal mediation analysis, and dynamic treatment\neffects. The models refer to studies of Froelich (2007)\n, Huber (2012)\n, Huber (2014)\n, Huber (2014)\n, Froelich and Huber (2017)\n, Hsu, Huber, Lee, and Lettry (2020)\n, and others.", "License": "MIT + file LICENSE", "Encoding": "UTF-8", "LazyData": "true", "RoxygenNote": "7.2.3", "NeedsCompilation": "no", "Packaged": { "Date": "2026-05-19 08:11:50 UTC", "User": "root" }, "Author": "Hugo Bodory [aut, cre] (ORCID:\n), Martin Huber [aut]\n(ORCID: ), Jannis Kueck\n[aut] (ORCID: )", "Config/pak/sysreqs": "make libicu-dev", "Repository": "https://hugometric.r-universe.dev", "Date/Publication": "2025-12-01 16:00:09 UTC", "RemoteUrl": "https://github.com/cran/causalweight", "RemoteRef": "HEAD", "RemoteSha": "e3a7d4c58912e1365d58bdf4a53333dfb9d3b0b8", "MD5sum": "d7ffa1e6f004ca7ac1ce022ec43bd00a", "_user": "hugometric", "_type": "src", "_file": "causalweight_1.1.4.tar.gz", "_fileid": "8cc8187cc592ae8f6232909ac95410fc5cf68c1c27249bee0b90646a2f4beb02", "_filesize": 2036230, "_sha256": "8cc8187cc592ae8f6232909ac95410fc5cf68c1c27249bee0b90646a2f4beb02", "_created": "2026-05-19T08:11:50.000Z", "_published": "2026-06-02T12:58:51.032Z", "_distro": "noble", "_jobs": [ { "job": 79075829427, "time": 163, "config": "linux-devel-x86_64", "r": "4.7.0", "check": "OK", "artifact": "7078230432" }, { "job": 79075829647, "time": 191, "config": "linux-release-x86_64", "r": "4.6.0", "check": "OK", "artifact": "7078239133" }, { "job": 79075830121, "time": 155, "config": "macos-oldrel-arm64", "r": "4.5.3", "check": "FAIL", "artifact": "" }, { "job": 79075829721, "time": 113, "config": "macos-release-arm64", "r": "4.6.0", "check": "FAIL", "artifact": "" }, { "job": 79075829452, "time": 194, "config": "source", "r": "4.6.0", "check": "OK", "artifact": "7078176175" }, { "job": 79075828822, "time": 117, "config": "wasm-release", "r": "4.6.0", "check": "OK", "artifact": "7358304726" }, { "job": 79075829587, "time": 126, "config": "windows-devel", "r": "4.7.0", "check": "OK", "artifact": "7078218526" }, { "job": 79075830163, "time": 110, "config": "windows-oldrel", "r": "4.5.3", "check": "OK", "artifact": "7078213170" }, { "job": 79075830016, "time": 106, "config": "windows-release", "r": "4.6.0", "check": "OK", "artifact": "7078212078" } ], "_buildurl": "https://github.com/r-universe/hugometric/actions/runs/26084658331", "_status": "success", "_host": "GitHub-Actions", "_upstream": "https://github.com/cran/causalweight", "_commit": { "id": "e3a7d4c58912e1365d58bdf4a53333dfb9d3b0b8", "author": "Hugo Bodory ", "committer": "cran-robot ", "message": "version 1.1.4\n", "time": 1764604809 }, "_maintainer": { "name": "Hugo Bodory", "email": "hugo.bodory@unisg.ch", "login": "hugometric", "description": "", "uuid": 24809337, "orcid": "0000-0002-3645-1204" }, "_registered": true, "_dependencies": [ { "package": "R", "version": ">= 3.5.0", "role": "Depends" }, { "package": "ranger", "role": "Depends" }, { "package": "mvtnorm", "role": "Imports" }, { "package": "np", "role": "Imports" }, { "package": "LARF", "role": "Imports" }, { "package": "hdm", "role": "Imports" }, { "package": "SuperLearner", "role": "Imports" }, { "package": "glmnet", "role": "Imports" }, { "package": "xgboost", "role": "Imports" }, { "package": "e1071", "role": "Imports" }, { "package": "fastDummies", "role": "Imports" }, { "package": "grf", "role": "Imports" }, { "package": "checkmate", "role": "Imports" }, { "package": "sandwich", "role": "Imports" } ], "_owner": "cran", "_selfowned": true, "_usedby": 0, "_updates": [ { "week": "2025-49", "n": 1 } ], "_tags": [ { "name": "1.1.4", "date": "2025-12-01" } ], "_stars": 2, "_contributors": [ { "user": "hugometric", "count": 10, "uuid": 24809337 } ], "_userbio": { "uuid": 24809337, "type": "user", "name": "Hugo Bodory" }, "_downloads": { "count": 755, "source": "https://cranlogs.r-pkg.org/downloads/total/last-month/causalweight" }, "_searchresults": 70, "_rbuild": "4.6.0", "_assets": [ "extra/causalweight.html", "extra/citation.cff", "extra/citation.html", "extra/citation.json", "extra/citation.txt", "extra/contents.json", "manual.pdf" ], "_realowner": "hugometric", "_cranurl": false, "_releases": [ { "version": "0.1.0", "date": "2018-02-23" }, { "version": "0.1.1", "date": "2018-08-01" }, { "version": "0.1.2", "date": "2018-12-01" }, { "version": "0.1.3", "date": "2019-12-05" }, { "version": "0.2.1", "date": "2020-06-15" }, { "version": "1.0.0", "date": "2020-12-02" }, { "version": "1.0.1", "date": "2021-04-16" }, { "version": "1.0.2", "date": "2021-07-08" }, { "version": "1.0.3", "date": "2022-08-12" }, { "version": "1.0.4", "date": "2023-05-04" }, { "version": "1.1.0", "date": "2024-01-24" }, { "version": "1.1.1", "date": "2024-07-23" }, { "version": "1.1.2", "date": "2025-03-22" }, { "version": "1.1.3", "date": "2025-03-26" }, { "version": "1.1.4", "date": "2025-12-01" } ], "_exports": [ "ATETDML", "attrlateweight", "didcontDML", "didcontDMLpanel", "didDML", "didweight", "dyntreatDML", "identificationDML", "ivnr", "lateweight", "medDML", "medlateweight", "medweight", "medweightcont", "paneltestDML", "RDDcovar", "testmedident", "treatDML", "treatselDML", "treatweight" ], "_datasets": [ { "name": "coffeeleaflet", "title": "Information leaflet on coffee production and environmental awareness of high school / university students in Bulgaria", "object": "coffeeleaflet", "class": [ "tbl_df", "tbl", "data.frame" ], "fields": [ "grade", "sex", "age", "mob", "bulgnationality", "langbulg", "mumage", "mumedu", "mumprof", "dadage", "dadedu", "dadprof", "material", "withbothpar", "withmum", "withdad", "withneither", "oldsiblings", "youngsiblings", "schoolmaths", "schoolrakdelsve", "schoolvazov", "schoolfinance", "schoolvarna", "schoolspanish", "schooltechuni", "schoolvidin", "schooluni", "citysofia", "cityvarna", "cityvidin", "treatment", "drinkcoffee", "cupsest", "devi_cupsest", "impworldecon", "impincome", "awarewaste", "awarepesticide", "awaredeforestation", "awarewastewater", "awarebiodiversityloss", "awareunfairworking", "reusepurposeful", "reusesoil", "choiceprice", "choicetastepleasure", "choiceenvironsocial" ], "rows": 522, "table": true, "tojson": true }, { "name": "coupon", "title": "Data on daily spending and coupon receipt (selective subsample) This data set is a selective subsample of the data set \"couponsretailer\" which was constructed for illustrative purposes.", "object": "coupon", "class": [ "data.frame" ], "fields": [ "dailyspending", "coupons", "coupons_preperiod", "dailyspending_preperiod", "income_bracket", "age_range", "married", "rented", "family_size" ], "rows": 1293, "table": true, "tojson": true }, { "name": "couponsretailer", "title": "Data on daily spending and coupon receipt A dataset containing information on the purchasing behavior of 1582 retail store customers across 32 coupon campaigns.", "object": "couponsretailer", "class": [ "data.frame" ], "fields": [ "customer_id", "period", "age_range", "married", "rented", "family_size", "income_bracket", "dailyspending_preperiod", "purchase_ReadyEatFood_preperiod", "purchase_MeatSeafood_preperiod", "purchase_OtherFood_preperiod", "purchase_Drugstore_preperiod", "purchase_OtherNonfood_preperiod", "coupons_Any_preperiod", "coupons_ReadyEatFood_preperiod", "coupons_MeatSeafood_preperiod", "coupons_OtherFood_preperiod", "coupons_Drugstore_preperiod", "coupons_OtherNonfood_preperiod", "coupons_Any_redeemed_preperiod", "coupons_Any", "coupons_ReadyEatFood", "coupons_MeatSeafood", "coupons_OtherFood", "coupons_Drugstore", "coupons_OtherNonfood", "dailyspending" ], "rows": 50624, "table": true, "tojson": true }, { "name": "creditcard", "title": "Expenditure and Default Data", "object": "creditcard", "class": [ "data.frame" ], "fields": [ "expenditure", "reports", "age", "income", "owner", "selfempl", "dependents", "months", "majorcards", "active" ], "rows": 1319, "table": true, "tojson": true }, { "name": "games", "title": "Sales of video games", "object": "games", "class": [ "data.frame" ], "fields": [ "name", "genre", "platform", "esrbrating", "publisher", "year", "metascore", "userscore", "sales" ], "rows": 3956, "table": true, "tojson": true }, { "name": "india", "title": "India's National Health Insurance Program (RSBY)", "object": "india", "class": [ "data.frame" ], "fields": [ "X", "village_id", "DistrictId", "treat", "mech", "enrolled", "EXPhosp_1" ], "rows": 11089, "table": true, "tojson": true }, { "name": "JC", "title": "Job Corps data", "object": "JC", "class": [ "data.frame" ], "fields": [ "assignment", "female", "age", "white", "black", "hispanic", "educ", "educmis", "geddegree", "hsdegree", "english", "cohabmarried", "haschild", "everwkd", "mwearn", "hhsize", "hhsizemis", "educmum", "educmummis", "educdad", "educdadmis", "welfarechild", "welfarechildmis", "health", "healthmis", "smoke", "smokemis", "alcohol", "alcoholmis", "everwkdy1", "earnq4", "earnq4mis", "pworky1", "pworky1mis", "health12", "health12mis", "trainy1", "trainy2", "pworky2", "pworky3", "pworky4", "earny2", "earny3", "earny4", "health30", "health48" ], "rows": 9240, "table": true, "tojson": true }, { "name": "labormarket", "title": "Temporary Work Agency (TWA) Assignments and Permanent Employment in Sicily", "object": "labormarket", "class": [ "data.frame" ], "fields": [ "age", "blu1", "child", "ct", "dist", "emp1", "hour1", "hour3", "id", "italy", "male", "me", "nofl1", "nyu1", "out3", "pa", "single", "tp", "train1", "treat", "unemp1", "wage1", "wage2", "wage3", "yu1", "white1", "unemp3", "nofl3", "edu0", "edu1", "edu2", "edu" ], "rows": 1120, "table": true, "tojson": true }, { "name": "rkd", "title": "Swedish municipalities", "object": "rkd", "class": [ "tbl_df", "tbl", "data.frame" ], "fields": [ "code", "year", "municipality", "pop_1", "pop", "partpop06", "partpop7_15", "partpop80_", "partforeign", "costequalgrants", "popchange_10y", "pers_admin", "pers_child", "pers_school", "pers_elder", "pers_total", "pers_social", "pers_tech", "expenditures_total", "wage_admin", "wage_child", "wage_school", "wage_elder", "wage_total", "wage_social", "wage_tech", "pers_officials", "pers_assistants", "pers_priv_school", "pers_priv_elder", "pers_priv_social", "pers_priv_child", "migrationgrant", "migpop", "summigpop", "sumpop", "migrationmean", "exp_total", "exp_admin", "exp_child", "exp_school", "exp_elder", "exp_social", "exp_tech", "expshare_total", "expshare_admin", "expshare_child", "expshare_school", "expshare_elder", "expshare_social", "expshare_tech", "outmigration", "forcing" ], "rows": 2511, "table": true, "tojson": true }, { "name": "swissexper", "title": "Correspondence test in Swiss apprenticeship market", "object": "swissexper", "class": [ "tbl_df", "tbl", "data.frame" ], "fields": [ "city", "foundatdate", "employees", "sector", "uniqueID", "sendatdate", "job_father", "job_mother", "tier", "hasmoved", "contgender", "letterback", "outcome_invite", "female_appl", "antidiscrpolicy", "outcome_interest", "gender_neutrality", "company_activity" ], "rows": 2928, "table": true, "tojson": true }, { "name": "ubduration", "title": "Austrian unemployment duration data", "object": "ubduration", "class": [ "data.frame" ], "fields": [ "y", "z", "marrstatus", "education", "foreign", "rr", "lwageljob", "experience", "whitecollar", "industry" ], "rows": 5659, "table": true, "tojson": true }, { "name": "wexpect", "title": "Wage expectations of students in Switzerland", "object": "wexpect", "class": [ "data.frame" ], "fields": [ "wexpect1", "wexpect2", "wexpect1othersex", "wexpect2othersex", "male", "business", "econ", "communi", "businform", "plansfull", "planseduc", "sectorcons", "sectortradesales", "sectortransware", "sectorhosprest", "sectorinfocom", "sectorfininsur", "sectorconsult", "sectoreduscience", "sectorhealthsocial", "typegenstratman", "typemarketing", "typecontrol", "typefinance", "typesales", "typetechengin", "typehumanres", "posmanager", "age", "swiss", "hassiblings", "motherhighedu", "fatherhighedu", "motherworkedfull", "motherworkedpart", "matwellbeing", "homeowner", "treatmentinformation", "treatmentorder" ], "rows": 804, "table": true, "tojson": true } ], "_help": [ { "page": "ATETDML", "title": "ATET Estimation for Binary Treatments using Double Machine Learning", "topics": [ "ATETDML" ] }, { "page": "attrlateweight", "title": "Local average treatment effect estimation in multiple follow-up periods with outcome attrition based on inverse probability weighting", "topics": [ "attrlateweight" ] }, { "page": "coffeeleaflet", "title": "Information leaflet on coffee production and environmental awareness of high school / university students in Bulgaria", "topics": [ "coffeeleaflet" ] }, { "page": "coupon", "title": "Data on daily spending and coupon receipt (selective subsample) This data set is a selective subsample of the data set \"couponsretailer\" which was constructed for illustrative purposes.", "topics": [ "coupon" ] }, { "page": "couponsretailer", "title": "Data on daily spending and coupon receipt A dataset containing information on the purchasing behavior of 1582 retail store customers across 32 coupon campaigns.", "topics": [ "couponsretailer" ] }, { "page": "creditcard", "title": "Expenditure and Default Data", "topics": [ "creditcard" ] }, { "page": "didcontDML", "title": "Continuous Difference-in-Differences using Double Machine Learning for Repeated Cross-Sections", "topics": [ "didcontDML" ] }, { "page": "didcontDMLpanel", "title": "Continuous Difference-in-Differences using Double Machine Learning for Panel Data", "topics": [ "didcontDMLpanel" ] }, { "page": "didDML", "title": "Difference-in-Differences in Repeated Cross-Sections for Binary Treatments using Double Machine Learning", "topics": [ "didDML" ] }, { "page": "didweight", "title": "Difference-in-differences based on inverse probability weighting", "topics": [ "didweight" ] }, { "page": "dyntreatDML", "title": "Dynamic treatment effect evaluation with double machine learning", "topics": [ "dyntreatDML" ] }, { "page": "games", "title": "Sales of video games", "topics": [ "games" ] }, { "page": "identificationDML", "title": "Testing identification with double machine learning", "topics": [ "identificationDML" ] }, { "page": "india", "title": "India's National Health Insurance Program (RSBY)", "topics": [ "india" ] }, { "page": "ivnr", "title": "Instrument-based treatment evaluation under endogeneity and non-response bias", "topics": [ "ivnr" ] }, { "page": "JC", "title": "Job Corps data", "topics": [ "JC" ] }, { "page": "labormarket", "title": "Temporary Work Agency (TWA) Assignments and Permanent Employment in Sicily", "topics": [ "labormarket" ] }, { "page": "lateweight", "title": "Local average treatment effect estimation based on inverse probability weighting", "topics": [ "lateweight" ] }, { "page": "medDML", "title": "Causal mediation analysis with double machine learning", "topics": [ "medDML" ] }, { "page": "medlateweight", "title": "Causal mediation analysis with instruments for treatment and mediator based on weighting", "topics": [ "medlateweight" ] }, { "page": "medweight", "title": "Causal mediation analysis based on inverse probability weighting with optional sample selection correction.", "topics": [ "medweight" ] }, { "page": "medweightcont", "title": "Causal mediation analysis with a continuous treatment based on weighting by the inverse of generalized propensity scores", "topics": [ "medweightcont" ] }, { "page": "paneltestDML", "title": "paneltestDML: Overidentification test for ATET estimation in panel data", "topics": [ "paneltestDML" ] }, { "page": "RDDcovar", "title": "Sharp regression discontinuity design conditional on covariates", "topics": [ "RDDcovar" ] }, { "page": "rkd", "title": "Swedish municipalities", "topics": [ "rkd" ] }, { "page": "swissexper", "title": "Correspondence test in Swiss apprenticeship market", "topics": [ "swissexper" ] }, { "page": "testmedident", "title": "Test for identification in causal mediation and dynamic treatment models", "topics": [ "testmedident" ] }, { "page": "treatDML", "title": "Binary or multiple discrete treatment effect evaluation with double machine learning", "topics": [ "treatDML" ] }, { "page": "treatselDML", "title": "Binary or multiple treatment effect evaluation with double machine learning under sample selection/outcome attrition", "topics": [ "treatselDML" ] }, { "page": "treatweight", "title": "Treatment evaluation based on inverse probability weighting with optional sample selection correction.", "topics": [ "treatweight" ] }, { "page": "ubduration", "title": "Austrian unemployment duration data", "topics": [ "ubduration" ] }, { "page": "wexpect", "title": "Wage expectations of students in Switzerland", "topics": [ "wexpect" ] } ], "_rundeps": [ "backports", "bitops", "boot", "caTools", "checkmate", "class", "cli", "codetools", "cpp11", "cubature", "cvAUC", "data.table", "DiceKriging", "e1071", "farver", "fastDummies", "foreach", "Formula", "gam", "ggplot2", "glmnet", "glue", "gplots", "grf", "gtable", "gtools", "hdm", "isoband", "iterators", "jsonlite", "KernSmooth", "labeling", "LARF", "lattice", "lifecycle", "lmtest", "magrittr", "MASS", "Matrix", "MatrixModels", "mvtnorm", "nnls", "np", "pillar", "pkgconfig", "proxy", "quadprog", "quantreg", "R6", "ranger", "RColorBrewer", "Rcpp", "RcppEigen", "rlang", "ROCR", "S7", "sandwich", "scales", "shape", "SparseM", "stringi", "stringr", "SuperLearner", "survival", "tibble", "utf8", "vctrs", "viridisLite", "withr", "xgboost", "zoo" ], "_score": 2.146128035678238, "_indexed": true, "_nocasepkg": "causalweight", "_universes": [ "hugometric" ], "_binaries": [ { "r": "4.7.0", "os": "linux", "version": "1.1.4", "date": "2026-05-19T08:14:11.000Z", "distro": "noble", "commit": "e3a7d4c58912e1365d58bdf4a53333dfb9d3b0b8", "fileid": "5730e80f9e122783a1e6a85936447297f0a9eb0bf1490e5f432085ce51093c7b", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/hugometric/actions/runs/26084658331" }, { "r": "4.6.0", "os": "linux", "version": "1.1.4", "date": "2026-05-19T08:14:37.000Z", "distro": "noble", "commit": "e3a7d4c58912e1365d58bdf4a53333dfb9d3b0b8", "fileid": "e045e43f3ceb04f8fc755bb3f9247d9ec502f37b4e75771725e0c50104ea6c67", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/hugometric/actions/runs/26084658331" }, { "r": "4.7.0", "os": "win", "version": "1.1.4", "date": "2026-05-19T08:13:24.000Z", "commit": "e3a7d4c58912e1365d58bdf4a53333dfb9d3b0b8", "fileid": "4f2662af426978f0e7a4184c762b75ad906de5b1d3956116adb131094b8e1e7c", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/hugometric/actions/runs/26084658331" }, { "r": "4.5.3", "os": "win", "version": "1.1.4", "date": "2026-05-19T08:13:12.000Z", "commit": "e3a7d4c58912e1365d58bdf4a53333dfb9d3b0b8", "fileid": "a21476de27903fc6bdd2da98f86d69ecd605f072d9a44379ae43ab2922b14972", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/hugometric/actions/runs/26084658331" }, { "r": "4.6.0", "os": "win", "version": "1.1.4", "date": "2026-05-19T08:13:10.000Z", "commit": "e3a7d4c58912e1365d58bdf4a53333dfb9d3b0b8", "fileid": "2a319eb1a0e812d5a4f9d79ef823f70392acde6031fb0e7931010dbea1da17ca", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/hugometric/actions/runs/26084658331" }, { "r": "4.6.0", "os": "wasm", "version": "1.1.4", "date": "2026-06-02T12:58:22.000Z", "commit": "e3a7d4c58912e1365d58bdf4a53333dfb9d3b0b8", "fileid": "251f5d2cf3d9818244b32ac26c215c9c6549f0b294c5bf015f44b357fcd3edd3", "status": "success", "buildurl": "https://github.com/r-universe/hugometric/actions/runs/26084658331" } ] }