DSGym/DSGym-Tasks huggingface dataset:
Context: The CSV file ihdp_1.csv contains data obtained from the Infant Health and Development Program (IHDP). The study is designed to evaluate the effect of home visit from specialist doctors on the cognitive test scores of premature infants. The confounders x (x1-x25) correspond to collected measurements of the children and their mothers, including measurements on the child (birth weight, head circumference, weeks born preterm, birth order, first born, neonatal health index, sex, twin status), as well as behaviors engaged in during the pregnancy (smoked cigarettes, drank alcohol, took drugs) and measurements on the mother at the time she gave birth (age, marital status, educational attainment, whether she worked during pregnancy, whether she received prenatal care) and the site (8 total) in which the family resided at the start of the intervention. There are 6 continuous covariates and 19 binary covariates.

Question: What is the Average treatment effect of the dataset? Please round to the nearest hundredth.
Answer: 4.05
Topics: Causality, Average treatment effect, Observational data

Question: What is the Average treatment effect on the control of the dataset? Please round to the nearest hundredth.
Answer: 4.06
Topics: Causality, Average treatment effect on the control, Observational data
Context: The CSV file ihdp_2.csv contains data obtained from the Infant Health and Development Program (IHDP). The study is designed to evaluate the effect of home visit from specialist doctors on the cognitive test scores of premature infants. The confounders x (x1-x25) correspond to collected measurements of the children and their mothers, including measurements on the child (birth weight, head circumference, weeks born preterm, birth order, first born, neonatal health index, sex, twin status), as well as behaviors engaged in during the pregnancy (smoked cigarettes, drank alcohol, took drugs) and measurements on the mother at the time she gave birth (age, marital status, educational attainment, whether she worked during pregnancy, whether she received prenatal care) and the site (8 total) in which the family resided at the start of the intervention. There are 6 continuous covariates and 19 binary covariates.

Question: What is the Average treatment effect of the dataset? Please round to the nearest hundredth.
Answer: 4.10
Topics: Causality, Average treatment effect, Observational data

Question: What is the Average treatment effect on the control of the dataset? Please round to the nearest hundredth.
Answer: 4.12
Topics: Causality, Average treatment effect on the control, Observational data
Context: The CSV file ihdp_3.csv contains data obtained from the Infant Health and Development Program (IHDP). The study is designed to evaluate the effect of home visit from specialist doctors on the cognitive test scores of premature infants. The confounders x (x1-x25) correspond to collected measurements of the children and their mothers, including measurements on the child (birth weight, head circumference, weeks born preterm, birth order, first born, neonatal health index, sex, twin status), as well as behaviors engaged in during the pregnancy (smoked cigarettes, drank alcohol, took drugs) and measurements on the mother at the time she gave birth (age, marital status, educational attainment, whether she worked during pregnancy, whether she received prenatal care) and the site (8 total) in which the family resided at the start of the intervention. There are 6 continuous covariates and 19 binary covariates.

Question: What is the Average treatment effect of the dataset? Please round to the nearest hundredth.
Answer: 4.27
Topics: Causality, Average treatment effect, Observational data

Question: What is the Average treatment effect on the treated of the dataset? Please round to the nearest hundredth.
Answer: 4.00
Topics: Causality, Average treatment effect on the treated, Observational data

Question: What is the Average treatment effect on the control of the dataset? Please round to the nearest hundredth.
Answer: 4.34
Topics: Causality, Average treatment effect on the control, Observational data
Context: The CSV file ihdp_4.csv contains data obtained from the Infant Health and Development Program (IHDP). The study is designed to evaluate the effect of home visit from specialist doctors on the cognitive test scores of premature infants. The confounders x (x1-x25) correspond to collected measurements of the children and their mothers, including measurements on the child (birth weight, head circumference, weeks born preterm, birth order, first born, neonatal health index, sex, twin status), as well as behaviors engaged in during the pregnancy (smoked cigarettes, drank alcohol, took drugs) and measurements on the mother at the time she gave birth (age, marital status, educational attainment, whether she worked during pregnancy, whether she received prenatal care) and the site (8 total) in which the family resided at the start of the intervention. There are 6 continuous covariates and 19 binary covariates.

Question: What is the Average treatment effect of the dataset? Please round to the nearest hundredth.
Answer: 4.16
Topics: Causality, Average treatment effect, Observational data

Question: What is the Average treatment effect on the treated of the dataset? Please round to the nearest hundredth.
Answer: 4.00
Topics: Causality, Average treatment effect on the treated, Observational data

Question: What is the Average treatment effect on the control of the dataset? Please round to the nearest hundredth.
Answer: 4.20
Topics: Causality, Average treatment effect on the control, Observational data
Context: The CSV file ihdp_5.csv contains data obtained from the Infant Health and Development Program (IHDP). The study is designed to evaluate the effect of home visit from specialist doctors on the cognitive test scores of premature infants. The confounders x (x1-x25) correspond to collected measurements of the children and their mothers, including measurements on the child (birth weight, head circumference, weeks born preterm, birth order, first born, neonatal health index, sex, twin status), as well as behaviors engaged in during the pregnancy (smoked cigarettes, drank alcohol, took drugs) and measurements on the mother at the time she gave birth (age, marital status, educational attainment, whether she worked during pregnancy, whether she received prenatal care) and the site (8 total) in which the family resided at the start of the intervention. There are 6 continuous covariates and 19 binary covariates.

Question: What is the Average treatment effect of the dataset? Please round to the nearest hundredth.
Answer: 4.00
Topics: Causality, Average treatment effect, Observational data

Question: What is the Average treatment effect on the treated of the dataset? Please round to the nearest hundredth.
Answer: 4.00
Topics: Causality, Average treatment effect on the treated, Observational data

Question: What is the Average treatment effect on the control of the dataset? Please round to the nearest hundredth.
Answer: 4.00
Topics: Causality, Average treatment effect on the control, Observational data
Context: The CSV file ihdp_6.csv contains data obtained from the Infant Health and Development Program (IHDP). The study is designed to evaluate the effect of home visit from specialist doctors on the cognitive test scores of premature infants. The confounders x (x1-x25) correspond to collected measurements of the children and their mothers, including measurements on the child (birth weight, head circumference, weeks born preterm, birth order, first born, neonatal health index, sex, twin status), as well as behaviors engaged in during the pregnancy (smoked cigarettes, drank alcohol, took drugs) and measurements on the mother at the time she gave birth (age, marital status, educational attainment, whether she worked during pregnancy, whether she received prenatal care) and the site (8 total) in which the family resided at the start of the intervention. There are 6 continuous covariates and 19 binary covariates.

Question: What is the Average treatment effect of the dataset? Please round to the nearest hundredth.
Answer: 3.99
Topics: Causality, Average treatment effect, Observational data

Question: What is the Average treatment effect on the control of the dataset? Please round to the nearest hundredth.
Answer: 3.99
Topics: Causality, Average treatment effect on the control, Observational data
Context: The CSV file ihdp_7.csv contains data obtained from the Infant Health and Development Program (IHDP). The study is designed to evaluate the effect of home visit from specialist doctors on the cognitive test scores of premature infants. The confounders x (x1-x25) correspond to collected measurements of the children and their mothers, including measurements on the child (birth weight, head circumference, weeks born preterm, birth order, first born, neonatal health index, sex, twin status), as well as behaviors engaged in during the pregnancy (smoked cigarettes, drank alcohol, took drugs) and measurements on the mother at the time she gave birth (age, marital status, educational attainment, whether she worked during pregnancy, whether she received prenatal care) and the site (8 total) in which the family resided at the start of the intervention. There are 6 continuous covariates and 19 binary covariates.

Question: What is the Average treatment effect of the dataset? Please round to the nearest hundredth.
Answer: 3.85
Topics: Causality, Average treatment effect, Observational data

Question: What is the Average treatment effect on the control of the dataset? Please round to the nearest hundredth.
Answer: 3.82
Topics: Causality, Average treatment effect on the control, Observational data
Context: The CSV file ihdp_8.csv contains data obtained from the Infant Health and Development Program (IHDP). The study is designed to evaluate the effect of home visit from specialist doctors on the cognitive test scores of premature infants. The confounders x (x1-x25) correspond to collected measurements of the children and their mothers, including measurements on the child (birth weight, head circumference, weeks born preterm, birth order, first born, neonatal health index, sex, twin status), as well as behaviors engaged in during the pregnancy (smoked cigarettes, drank alcohol, took drugs) and measurements on the mother at the time she gave birth (age, marital status, educational attainment, whether she worked during pregnancy, whether she received prenatal care) and the site (8 total) in which the family resided at the start of the intervention. There are 6 continuous covariates and 19 binary covariates.

Question: What is the Average treatment effect of the dataset? Please round to the nearest hundredth.
Answer: 10.47
Topics: Causality, Average treatment effect, Observational data

Question: What is the Average treatment effect on the treated of the dataset? Please round to the nearest hundredth.
Answer: 4.00
Topics: Causality, Average treatment effect on the treated, Observational data

Question: What is the Average treatment effect on the control of the dataset? Please round to the nearest hundredth.
Answer: 11.94
Topics: Causality, Average treatment effect on the control, Observational data
Context: The CSV file ihdp_9.csv contains data obtained from the Infant Health and Development Program (IHDP). The study is designed to evaluate the effect of home visit from specialist doctors on the cognitive test scores of premature infants. The confounders x (x1-x25) correspond to collected measurements of the children and their mothers, including measurements on the child (birth weight, head circumference, weeks born preterm, birth order, first born, neonatal health index, sex, twin status), as well as behaviors engaged in during the pregnancy (smoked cigarettes, drank alcohol, took drugs) and measurements on the mother at the time she gave birth (age, marital status, educational attainment, whether she worked during pregnancy, whether she received prenatal care) and the site (8 total) in which the family resided at the start of the intervention. There are 6 continuous covariates and 19 binary covariates.

Question: What is the Average treatment effect of the dataset? Please round to the nearest hundredth.
Answer: 4.59
Topics: Causality, Average treatment effect, Observational data

Question: What is the Average treatment effect on the control of the dataset? Please round to the nearest hundredth.
Answer: 4.72
Topics: Causality, Average treatment effect on the control, Observational data
Context: The CSV file jobs_0.csv contains data obtained from the National Supported Work program. The study is designed to evaluate the effect of job training (t) on the income and employment status after training (y). The confounders x (x0-x16) correspond to covariates such as age and education, as well as previous earnings.

Question: Please estimate the Average treatment effect on the treated (ATT) of the dataset. Please round to the nearest thousandth.
Answer: 0.074
Topics: Causality, Average treatment effect on the treated, Observational data
Context: The CSV file jobs_1.csv contains data obtained from the National Supported Work program. The study is designed to evaluate the effect of job training (t) on the income and employment status after training (y). The confounders x (x0-x16) correspond to covariates such as age and education, as well as previous earnings.

Question: Please estimate the Average treatment effect on the treated (ATT) of the dataset. Please round to the nearest thousandth.
Answer: 0.025
Topics: Causality, Average treatment effect on the treated, Observational data
Context: The CSV file jobs_2.csv contains data obtained from the National Supported Work program. The study is designed to evaluate the effect of job training (t) on the income and employment status after training (y). The confounders x (x0-x16) correspond to covariates such as age and education, as well as previous earnings.

Question: Please estimate the Average treatment effect on the treated (ATT) of the dataset. Please round to the nearest thousandth.
Answer: 0.081
Topics: Causality, Average treatment effect on the treated, Observational data
Context: The CSV file jobs_3.csv contains data obtained from the National Supported Work program. The study is designed to evaluate the effect of job training (t) on the income and employment status after training (y). The confounders x (x0-x16) correspond to covariates such as age and education, as well as previous earnings.

Question: Please estimate the Average treatment effect on the treated (ATT) of the dataset. Please round to the nearest thousandth.
Answer: 0.110
Topics: Causality, Average treatment effect on the treated, Observational data
Context: The CSV file jobs_4.csv contains data obtained from the National Supported Work program. The study is designed to evaluate the effect of job training (t) on the income and employment status after training (y). The confounders x (x0-x16) correspond to covariates such as age and education, as well as previous earnings.

Question: Please estimate the Average treatment effect on the treated (ATT) of the dataset. Please round to the nearest thousandth.
Answer: 0.075
Topics: Causality, Average treatment effect on the treated, Observational data
Context: The CSV file jobs_5.csv contains data obtained from the National Supported Work program. The study is designed to evaluate the effect of job training (t) on the income and employment status after training (y). The confounders x (x0-x16) correspond to covariates such as age and education, as well as previous earnings.

Question: Please estimate the Average treatment effect on the treated (ATT) of the dataset. Please round to the nearest thousandth.
Answer: 0.097
Topics: Causality, Average treatment effect on the treated, Observational data
Context: The CSV file jobs_6.csv contains data obtained from the National Supported Work program. The study is designed to evaluate the effect of job training (t) on the income and employment status after training (y). The confounders x (x0-x16) correspond to covariates such as age and education, as well as previous earnings.

Question: Please estimate the Average treatment effect on the treated (ATT) of the dataset. Please round to the nearest thousandth.
Answer: 0.100
Topics: Causality, Average treatment effect on the treated, Observational data
Context: The CSV file jobs_7.csv contains data obtained from the National Supported Work program. The study is designed to evaluate the effect of job training (t) on the income and employment status after training (y). The confounders x (x0-x16) correspond to covariates such as age and education, as well as previous earnings.

Question: Please estimate the Average treatment effect on the treated (ATT) of the dataset. Please round to the nearest thousandth.
Answer: 0.075
Topics: Causality, Average treatment effect on the treated, Observational data
Context: The CSV file jobs_8.csv contains data obtained from the National Supported Work program. The study is designed to evaluate the effect of job training (t) on the income and employment status after training (y). The confounders x (x0-x16) correspond to covariates such as age and education, as well as previous earnings.

Question: Please estimate the Average treatment effect on the treated (ATT) of the dataset. Please round to the nearest thousandth.
Answer: 0.094
Topics: Causality, Average treatment effect on the treated, Observational data
Context: The CSV file jobs_9.csv contains data obtained from the National Supported Work program. The study is designed to evaluate the effect of job training (t) on the income and employment status after training (y). The confounders x (x0-x16) correspond to covariates such as age and education, as well as previous earnings.

Question: Please estimate the Average treatment effect on the treated (ATT) of the dataset. Please round to the nearest thousandth.
Answer: 0.073
Topics: Causality, Average treatment effect on the treated, Observational data
Context: To estimate the impacts of online class format on exam outcomes, the dataset online_classroom.csv was used to compare students in online classes versus face-to-face classes. In this analysis, the treatment effect is measured as the difference in exam scores between the two groups. Each row of the dataset contains a student's exam outcome (the variable falsexam), their classroom format (online, face-to-face, or blended), and other variables like gender and ethnicity.

Question: What is the average treatment effect (ATE) of taking classes online on exam scores? Please use linear regression analysis considering only the students in face-to-face (format_blended = 0 and format_ol = 0) and online classes (format_ol = 1), do not consider other variables, and provide the ATE rounded to the nearest hundredth.
Answer: -4.91
Topics: Causality, Observational data, Linear regression, Average treatment effect
Context: To estimate the impact of an additional year of education on hourly wage, we look at a sample size representing individuals with varying levels of education and their hourly wages. The dataset wage.csv contains the necessary data for our analysis. The columns in this dataset include 'wage', representing the total income; 'hours', representing the total hours worked; and 'educ', representing the years of education; and other variables like the parents' education and the person's IQ score.

Question: What percentage increase in hourly wage can be expected for each additional year of education, based on the data described? Please use linear regression and do not consider variables other than 'wage', 'hours', and 'educ'. Please provide your answer to the nearest hundredth of a percent.
Answer: 5.36%
Topics: Causality, Observational data, Regression analysis

Question: What percentage increase in hourly wage can be expected for each additional year of education, based on the data described? Please use linear regression and take all provided variables into consideration. Please provide your answer to the nearest hundredth of a percent.
Answer: 4.11%
Topics: Causality, Observational data, Regression analysis
Context: To estimate the impact of education on hourly wage, we look at a sample size representing individuals with varying levels of education and their hourly wages. The dataset wage.csv contains the necessary data for our analysis. The columns in this dataset include 'wage', representing the total income; 'hours', representing the total hours worked; and 'educ', representing the years of education; and other variables like the parents' education and the person's IQ score.

Question: Please estimate the effect of graduating 12th grade on hourly wage. Please use linear regression and do not consider variables other than 'wage', 'hours', and 'educ'. Please provide your answer to the nearest hundredth.
Answer: 4.90
Topics: Causality, Observational data, Regression analysis
Context: In a study to determine the causal effect of sending an email reminder on the repayment of debts, a fintech company conducted a random test involving 5000 customers who were late on their payments. Each customer was randomly assigned to either receive an email about negotiating their debt or to be part of a control group that did not receive the email. Data was collected on the amounts paid by the late customers after this intervention. The dataset collections_email.csv contains variables including the amount paid (`payments`), whether the email was sent (`email`), whether the email was opened (`opened`), whether the customer contacted the collections department to negotiate their debt after having received the email (`agreement`), the customer's credit limit before being late (`credit_limit`), and the customer's risk score prior to the delivery of the email (`risk_score`).

Question: What is the average treatment effect (ATE) on payments from sending the email reminder to late-paying customers? Please choose the variables to adjust for, conduct linear regression, and round your answer to the nearest hundredth.
Answer: 4.43
Topics: Causality, Randomized experiment, Regression analysis, Average treatment effect, Confounder selection
Context: The dataset hospital_treatment.csv includes data from a randomized trial conducted by two hospitals. Both of them are conducting randomised trials on a new drug to treat a certain illness. The outcome of interest is days hospitalised. If the treatment is effective, it will lower the amount of days the patient stays in the hospital. For one of the hospitals, the policy regarding the random treatment is to give it to 90% of its patients while 10% get a placebo. The other hospital has a different policy: it gives the drug to a random 10% of its patients and 90% get a placebo. You are also told that the hospital that gives 90% of the true drug and 10% of placebo usually gets more severe cases of the illness to treat. The CSV file contains columns for `hospital` indicating the hospital a patient belongs to, `treatment` signifying if the patient received the new drug or placebo, `severity` reflecting the severity of the illness, and `days` representing the number of days the patient was hospitalized.

Question: What is the average treatment effect (ATE) of the new drug on the amount of days the patient stays in the hospital? Please choose the variables to adjust for, conduct linear regression, and round your answer to the nearest hundredth. If the new drug reduces the amount of days the patient stays in the hospital, the answer should be negative.
Answer: -7.59
Topics: Causality, Randomized experiment, Regression analysis, Average treatment effect, Confounder selection

Question: We are estimating the average treatment effect (ATE) of the new drug on the amount of days the patient stays in the hospital, and we already controlled for the severity. Should we also control for the 'hospital' variable? Please answer with 'yes' or 'no'.
Answer: no
Topics: Causality, Randomized experiment, Regression analysis, Average treatment effect, Confounder selection