{"args":{},"checks":[{"abs_tol":null,"bind":"any","column":null,"failure_class":null,"field":"value","interval":false,"key":null,"kind":"answer_backed","min_lre":null,"note":"every number of the answer is stated by a tool call","open_interval":false,"rel_tol":null,"required":true,"source_id":null,"table":null,"tool":null,"value":null,"weight":1.0,"where":null},{"abs_tol":null,"bind":"answer_call","column":null,"failure_class":null,"field":"value","interval":true,"key":null,"kind":"answer_figure_close","min_lre":null,"note":"QRData eval.py: strictly inside gold \u00b1 3% (a '%' gold divided by 100), on the answer figure after meta.answer_format","open_interval":true,"rel_tol":null,"required":true,"source_id":"qrdata.gold.371","table":null,"tool":null,"value":[0.102529,0.10887100000000001],"weight":1.0,"where":null}],"datasets":{"invest_email_rnd_test":{"columns":null,"description":"Data file invest_email_rnd_test.csv of this question; the question's data description is the description of dataset 'invest_email_rnd_train'.","file_sha256":"3c3a97cca6fdd96f6856f7856b5f8bc786a31d1a9be78c9f610ef3070783e970","format":"csv","frame_sha256":null,"generator":null,"kind":"file","licence":"CC BY-NC 4.0","member":"data/invest_email_rnd_test.csv","object":null,"params":{},"path":null,"read_kw":{},"records":null,"role":"agent","sha256":"3c3a97cca6fdd96f6856f7856b5f8bc786a31d1a9be78c9f610ef3070783e970","transforms":[],"url":"https://raw.githubusercontent.com/xxxiaol/QRData/de450af45ff7101b328bb064c6b475f73414a7ed/benchmark/data.zip"},"invest_email_rnd_train":{"columns":null,"description":"To evaluate the effectiveness of email marketing on customer investment decisions, the dataset invest_email_rnd_train.csv is used for training, and invest_email_rnd_train.csv is used for testing. They contain customer data including id, age, income, insurance, invested amount, binary indicators for whether they received three different emails (em1, em2, em3), and a binary outcome variable indicating whether the customer converted (invested vs. didn't invest) after receiving an email. The goal is to personalize email marketing by sending email-1 only to customers predicted to have the highest increase in the probability of conversion.","file_sha256":"3c3a97cca6fdd96f6856f7856b5f8bc786a31d1a9be78c9f610ef3070783e970","format":"csv","frame_sha256":null,"generator":null,"kind":"file","licence":"CC BY-NC 4.0","member":"data/invest_email_rnd_train.csv","object":null,"params":{},"path":null,"read_kw":{},"records":null,"role":"agent","sha256":"3c3a97cca6fdd96f6856f7856b5f8bc786a31d1a9be78c9f610ef3070783e970","transforms":[],"url":"https://raw.githubusercontent.com/xxxiaol/QRData/de450af45ff7101b328bb064c6b475f73414a7ed/benchmark/data.zip"}},"difficulty":"M","id":"qrdata_socsci.371","level":"T2","meta":{"agent_reach":{"blocked_by":["no_reference_path","no_tool:ml_cate"],"dtypes":{"invest_email_rnd_test":{"converted":"int64","em1":"int64"},"invest_email_rnd_train":{"converted":"int64","em1":"int64"}},"named":{"outcome":["converted"],"treatment":["em1"]},"reachable":false,"rule":"magi_bench.reach/1"},"answer_format":{"figure_transform":"identity","round_to":4,"unit_scale":1.0},"answer_format_from":{"figure_transform":null,"round_to":"as a percentage, rounded to two decimal places","unit_scale":"1.0: an estimator's figure is on the gold's own scale (a '%' gold is a fraction to eval.py, as is a coefficient)"},"answer_percent":"%","cluster":"invest_email_rnd_train.csv","family":"numerical","headline_keys":{},"naive":{"dataset":"invest_email_rnd_train","treatment":"em1","y":"converted"},"native":{"answer":"10.57%","keywords":["Causality","Observational data","Conditional Average Treatment Effect","Target transformation"],"multiple_choices":null,"question_type":"numerical","reference":"Causal Inference for the Brave and True 20"},"native_id":371,"reference":{"cite":1,"note":"Sem caminho de refer\u00eancia com as ferramentas registradas: no_tool:ml_cate. A resposta n\u00e3o cita estimativa de efeito.","steps":[{"args":{"dataset":"invest_email_rnd_train"},"tool":"data.describe","why":"Sem caminho de refer\u00eancia com as ferramentas registradas: no_tool:ml_cate."}]},"reference_path":{"calls":[],"needs":["no_tool:ml_cate"],"provenance":"none","quotes":{},"reference_datasets":[],"source_id":null},"reference_why":null},"question":"Please train a boosted tree model to predict the conditional average treatment effect (CATE) of sending email-1 (em1) on the conversion rate, using demographic and financial attributes of the customers. What is the predicted increase in the probability that customer 6958 will purchase the investment product, if they are sent email-1, according to the CATE estimate provided by the model? Please provide the answer as a percentage, rounded to two decimal places.","requires":[],"runner":null,"sources":["qrdata.gold.371","qrdata.eval_py"],"split":"public","suite":"qrdata_socsci","tags":["econ-bench","external","qrdata","brave_and_true","numerical","no_reference_path","naive"],"target":"agent","tool":null,"truth":"QRData gold: benchmark/QRData.json item 371 at de450af45ff7101b328bb064c6b475f73414a7ed"}