A Survey on Causal Inference <s> Datasets for Section 3. <s> The Infant Health and Development Program is a randomized clinical trial to test the efficacy of educational and family support services and pediatric follow-up, offered during the first 3 years of life, in reducing the incidence of developmental delay in low birth weight preterm infants at eight clinical sites (N = 985). It was hypothesized that larger intervention effects would be found for the domains in which low birth weight preterm infants are known to have problems, specifically visual-motor and spatial skills and receptive language skills. These analyses explore the effects of the Infant Health and Development Program on different domains of cognitive functioning. Cognitive domains are identified by means of factor analysis of the intelligence tests used at 12, 24, and 36 months (Bayley Scales of Infant Development (including the Mental and Motor scales) at 12 and 24 months; the Stanford-Binet, Peabody Picture Vocabulary Test, and Beery Test of Visual Motor Intergration at 36 months). Our results reveal that, although intervention benefits accrue across cognitive domains at 24 and 36 months, gains are most pronounced for receptive language and visual-motor and spatial skills. <s> BIB001 </s> A Survey on Causal Inference <s> Datasets for Section 3. <s> This paper considers causal inference and sample selection bias in nonexperimental settings in which (i) few units in the nonexperimental comparison group are comparable to the treatment units, and (ii) selecting a subset of comparison units similar to the treatment units is difficult because units must be compared across a high-dimensional set of pretreatment characteristics. We discuss the use of propensity score-matching methods, and implement them using data from the National Supported Work experiment. Following LaLonde (1986), we pair the experimental treated units with nonexperimental comparison units from the CPS and PSID, and compare the estimates of the treatment effect obtained using our methods to the benchmark results from the experiment. For both comparison groups, we show that the methods succeed in focusing attention on the small subset of the comparison units comparable to the treated units and, hence, in alleviating the bias due to systematic differences between the treated and comparison units. © 2002 by the President and Fellows of Harvard College and the Massachusetts Institute of Technology <s> BIB002 </s> A Survey on Causal Inference <s> Datasets for Section 3. <s> Low birth weight (LBW) infants experience severe health and developmental difficulties that can impose large costs on society. However, estimates of the return to LBW-prevention from cross-sectional associations may be biased by omitted variables, such as genetic factors. To address this, we compare the hospital costs, health at birth, and infant mortality rates between heavier and lighter infants from all twin pairs born in the United States. We also examine the effect of maternal smoking during pregnancy—the leading risk factor for LBW in the United States—on health among singleton births after controlling for detailed background characteristics. Both analyses imply substantially smaller effects of LBW per se than previously thought, suggesting two possibilities: 1) existing estimates overstate the true costs and consequences of LBW by at least a factor of four and by as much as a factor of twenty; or 2) different LBW-preventing interventions have different health and cost consequences, implying that policy efforts that presume a single return to reducing LBW will be suboptimal. <s> BIB003 </s> A Survey on Causal Inference <s> Datasets for Section 3. <s> Researchers have long struggled to identify causal effects in nonexperimental settings. Many recently proposed strategies assume ignorability of the treatment assignment mechanism and require fitting two models—one for the assignment mechanism and one for the response surface. This article proposes a strategy that instead focuses on very flexibly modeling just the response surface using a Bayesian nonparametric modeling procedure, Bayesian Additive Regression Trees (BART). BART has several advantages: it is far simpler to use than many recent competitors, requires less guesswork in model fitting, handles a large number of predictors, yields coherent uncertainty intervals, and fluidly handles continuous treatment variables and missing data for the outcome variable. BART also naturally identifies heterogeneous treatment effects. BART produces more accurate estimates of average treatment effects compared to propensity score matching, propensity-weighted estimators, and regression adjustment in the nonlinear ... <s> BIB004 </s> A Survey on Causal Inference <s> Datasets for Section 3. <s> Objective: We sought to develop an intensive care unit research database applying automated techniques to aggregate high-resolution diagnostic and therapeutic data from a large, diverse population of adult intensive care unit patients. This freely available database is intended to support epidemiologic research in critical care medicine and serve as a resource to evaluate new clinical decision support and monitoring algorithms. Design: Data collection and retrospective analysis. Setting: All adult intensive care units (medical intensive care unit, surgical intensive care unit, cardiac care unit, cardiac surgery recovery unit) at a tertiary care hospital. Patients: Adult patients admitted to intensive care units between 2001 and 2007. Interventions: None. Measurements and Main Results: The Multiparameter Intelligent Monitoring in Intensive Care II (MIMIC-II) database consists of 25,328 intensive care unit stays. The investigators collected detailed information about intensive care unit patient stays, including laboratory data, therapeutic intervention profiles such as vasoactive medication drip rates and ventilator settings, nursing progress notes, discharge summaries, radiology reports, provider order entry data, International Classification of Diseases, 9th Revision codes, and, for a subset of patients, high-resolution vital sign trends and waveforms. Data were automatically deidentified to comply with Health Insurance Portability and Accountability Act standards and integrated with relational database software to create electronic intensive care unit records for each patient stay. The data were made freely available in February 2010 through the Internet along with a detailed user’s guide and an assortment of data processing tools. The overall hospital mortality rate was 11.7%, which varied by critical care unit. The median intensive care unit length of stay was 2.2 days (interquartile range, 1.1‐4.4 days). According to the primary International Classification of Diseases, 9th Revision codes, the following disease categories each comprised at least 5% of the case records: diseases of the circulatory system (39.1%); trauma (10.2%); diseases of the digestive system (9.7%); pulmonary diseases (9.0%); infectious diseases (7.0%); and neoplasms (6.8%). Conclusions: MIMIC-II documents a diverse and very large population of intensive care unit patient stays and contains comprehensive and detailed clinical data, including physiological waveforms and minute-by-minute trends for a subset of records. It establishes a new public-access resource for critical care research, supporting a diverse range of analytic studies spanning epidemiology, clinical decision-rule development, and electronic tool development. (Crit Care Med 2011; 39:952‐960) <s> BIB005 </s> A Survey on Causal Inference <s> Datasets for Section 3. <s> The Cancer Genome Atlas (TCGA) Research Network has profiled and analyzed large numbers of human tumors to discover molecular aberrations at the DNA, RNA, protein and epigenetic levels. The resulting rich data provide a major opportunity to develop an integrated picture of commonalities, differences and emergent themes across tumor lineages. The Pan-Cancer initiative compares the first 12 tumor types profiled by TCGA. Analysis of the molecular aberrations and their functional roles across tumor types will teach us how to extend therapies effective in one cancer type to others with a similar genomic profile. <s> BIB006 </s> A Survey on Causal Inference <s> Datasets for Section 3. <s> Correlation implies association, but not causation. Conversely, causation implies association, but not correlation. <s> BIB007 </s> A Survey on Causal Inference <s> Datasets for Section 3. <s> Observational studies are rising in importance due to the widespread accumulation of data in fields such as healthcare, education, employment and ecology. We consider the task of answering counterfactual questions such as, "Would this patient have lower blood sugar had she received a different medication?". We propose a new algorithmic framework for counterfactual inference which brings together ideas from domain adaptation and representation learning. In addition to a theoretical justification, we perform an empirical comparison with previous approaches to causal inference from observational data. Our deep learning algorithm significantly outperforms the previous state-of-the-art. <s> BIB008 </s> A Survey on Causal Inference <s> Datasets for Section 3. <s> There is intense interest in applying machine learning to problems of causal inference in fields such as healthcare, economics and education. In particular, individual-level causal inference has important applications such as precision medicine. We give a new theoretical analysis and family of algorithms for predicting individual treatment effect (ITE) from observational data, under the assumption known as strong ignorability. The algorithms learn a "balanced" representation such that the induced treated and control distributions look similar. We give a novel, simple and intuitive generalization-error bound showing that the expected ITE estimation error of a representation is bounded by a sum of the standard generalization-error of that representation and the distance between the treated and control distributions induced by the representation. We use Integral Probability Metrics to measure distances between distributions, deriving explicit bounds for the Wasserstein and Maximum Mean Discrepancy (MMD) distances. Experiments on real and simulated data show the new algorithms match or outperform the state-of-the-art. <s> BIB009 </s> A Survey on Causal Inference <s> Datasets for Section 3. <s> Learning individual-level causal effects from observational data, such as inferring the most effective medication for a specific patient, is a problem of growing importance for policy makers. The most important aspect of inferring causal effects from observational data is the handling of confounders, factors that affect both an intervention and its outcome. A carefully designed observational study attempts to measure all important confounders. However, even if one does not have direct access to all confounders, there may exist noisy and uncertain measurement of proxies for confounders. We build on recent advances in latent variable modeling to simultaneously estimate the unknown latent space summarizing the confounders and the causal effect. Our method is based on Variational Autoencoders (VAE) which follow the causal structure of inference with proxies. We show our method is significantly more robust than existing methods, and matches the state-of-the-art on previous benchmarks focused on individual treatment effects. <s> BIB010 </s> A Survey on Causal Inference <s> Datasets for Section 3. <s> Predicated on the increasing abundance of electronic health records, we investi- gate the problem of inferring individualized treatment effects using observational data. Stemming from the potential outcomes model, we propose a novel multi- task learning framework in which factual and counterfactual outcomes are mod- eled as the outputs of a function in a vector-valued reproducing kernel Hilbert space (vvRKHS). We develop a nonparametric Bayesian method for learning the treatment effects using a multi-task Gaussian process (GP) with a linear coregion- alization kernel as a prior over the vvRKHS. The Bayesian approach allows us to compute individualized measures of confidence in our estimates via pointwise credible intervals, which are crucial for realizing the full potential of precision medicine. The impact of selection bias is alleviated via a risk-based empirical Bayes method for adapting the multi-task GP prior, which jointly minimizes the empirical error in factual outcomes and the uncertainty in (unobserved) counter- factual outcomes. We conduct experiments on observational datasets for an inter- ventional social program applied to premature infants, and a left ventricular assist device applied to cardiac patients wait-listed for a heart transplant. In both experi- ments, we show that our method significantly outperforms the state-of-the-art. <s> BIB011 </s> A Survey on Causal Inference <s> Datasets for Section 3. <s> We propose a novel approach for inferring the individualized causal effects of a treatment (intervention) from observational data. Our approach conceptualizes causal inference as a multitask learning problem; we model a subject's potential outcomes using a deep multitask network with a set of shared layers among the factual and counterfactual outcomes, and a set of outcome-specific layers. The impact of selection bias in the observational data is alleviated via a propensity-dropout regularization scheme, in which the network is thinned for every training example via a dropout probability that depends on the associated propensity score. The network is trained in alternating phases, where in each phase we use the training examples of one of the two potential outcomes (treated and control populations) to update the weights of the shared layers and the respective outcome-specific layers. Experiments conducted on data based on a real-world observational study show that our algorithm outperforms the state-of-the-art. <s> BIB012 </s> A Survey on Causal Inference <s> Datasets for Section 3. <s> Statisticians have made great progress in creating methods that ::: reduce our reliance on parametric assumptions. However, this explosion in ::: research has resulted in a breadth of inferential strategies that both create ::: opportunities for more reliable inference as well as complicate the choices ::: that an applied researcher has to make and defend. Relatedly, researchers advocating ::: for new methods typically compare their method to at best 2 or 3 ::: other causal inference strategies and test using simulations that may or may ::: not be designed to equally tease out flaws in all the competing methods. The ::: causal inference data analysis challenge, “Is Your SATT Where It’s At?”, ::: launched as part of the 2016 Atlantic Causal Inference Conference, sought to ::: make progress with respect to both of these issues. The researchers creating ::: the data testing grounds were distinct from the researchers submitting methods ::: whose efficacy would be evaluated. Results from 30 competitors across ::: the two versions of the competition (black-box algorithms and do-it-yourself ::: analyses) are presented along with post-hoc analyses that reveal information ::: about the characteristics of causal inference strategies and settings that affect ::: performance. The most consistent conclusion was that methods that flexibly ::: model the response surface perform better overall than methods that fail to ::: do so. Finally new methods are proposed that combine features of several of ::: the top-performing submitted methods. <s> BIB013 </s> A Survey on Causal Inference <s> Datasets for Section 3. <s> SummaryDrawing causal inference with observational studies is the central pillar of many disciplines. One sufficient condition for identifying the causal effect is that the treatment-outcome relationship is unconfounded conditional on the observed covariates. It is often believed that the more covariates we condition on, the more plausible this unconfoundedness assumption is. This belief has had a huge impact on practical causal inference, suggesting that we should adjust for all pretreatment covariates. However, when there is unmeasured confounding between the treatment and outcome, estimators adjusting for some pretreatment covariate might have greater bias than estimators that do not adjust for this covariate. This kind of covariate is called a bias amplifier, and includes instrumental variables that are independent of the confounder and affect the outcome only through the treatment. Previously, theoretical results for this phenomenon have been established only for linear models. We fill this gap in the literature by providing a general theory, showing that this phenomenon happens under a wide class of models satisfying certain monotonicity assumptions. <s> BIB014 </s> A Survey on Causal Inference <s> Datasets for Section 3. <s> Estimating individual treatment effect (ITE) is a challenging problem in causal inference, due to missing counterfactuals and the selection bias. Existing ITE estimation methods mainly focus on balancing the distributions of control and treated groups, but ignore the local similarity information that is helpful. In this paper, we propose a local similarity preserved individual treatment effect (SITE) estimation method based on deep representation learning. SITE preserves local similarity and balances data distributions simultaneously, by focusing on several hard samples in each mini-batch. Experimental results on synthetic and three real-world datasets demonstrate the advantages of the proposed SITE method, compared with the state-of-the-art ITE estimation methods. <s> BIB015 </s> A Survey on Causal Inference <s> Datasets for Section 3. <s> Estimating individualized treatment effects (ITE) is a challenging task due to the need for an individual's potential outcomes to be learned from biased data and without having access to the counterfactuals. We propose a novel method for inferring ITE based on the Generative Adversarial Nets (GANs) framework. Our method, termed Generative Adversarial Nets for inference of Individualized Treatment Effects (GANITE), is motivated by the possibility that we can capture the uncertainty in the counterfactual distributions by attempting to learn them using a GAN. We generate proxies of the counterfactual outcomes using a counterfactual generator, G, and then pass these proxies to an ITE generator, I, in order to train it. By modeling both of these using the GAN framework, we are able to infer based on the factual data, while still accounting for the unseen counterfactuals. We test our method on three real-world datasets (with both binary and multiple treatments) and show that GANITE outperforms state-of-the-art methods. <s> BIB016 </s> A Survey on Causal Inference <s> Datasets for Section 3. <s> Causal inference analysis is the estimation of the effects of actions on outcomes. In the context of healthcare data this means estimating the outcome of counter-factual treatments (i.e. including treatments that were not observed) on a patient's outcome. Compared to classic machine learning methods, evaluation and validation of causal inference analysis is more challenging because ground truth data of counter-factual outcome can never be obtained in any real-world scenario. Here, we present a comprehensive framework for benchmarking algorithms that estimate causal effect. The framework includes unlabeled data for prediction, labeled data for validation, and code for automatic evaluation of algorithm predictions using both established and novel metrics. The data is based on real-world covariates, and the treatment assignments and outcomes are based on simulations, which provides the basis for validation. In this framework we address two questions: one of scaling, and the other of data-censoring. The framework is available as open source code at this https URL <s> BIB017 </s> A Survey on Causal Inference <s> Datasets for Section 3. <s> Observational data is increasingly used as a means for making individual-level causal predictions and intervention recommendations. The foremost challenge of causal inference from observational data is hidden confounding, whose presence cannot be tested in data and can invalidate any causal conclusion. Experimental data does not suffer from confounding but is usually limited in both scope and scale. We introduce a novel method of using limited experimental data to correct the hidden confounding in causal effect models trained on larger observational data, even if the observational data does not fully overlap with the experimental data. Our method makes strictly weaker assumptions than existing approaches, and we prove conditions under which it yields a consistent estimator. We demonstrate our method's efficacy using real-world data from a large educational experiment. <s> BIB018 </s> A Survey on Causal Inference <s> Datasets for Section 3. <s> This brief note documents the data generating processes used in the 2017 Data Analysis Challenge associated with the Atlantic Causal Inference Conference (ACIC). The focus of the challenge was estimation and inference for conditional average treatment effects (CATEs) in the presence of targeted selection, which leads to strong confounding. The associated data files and further plots can be found on the first author's web page. <s> BIB019 </s> A Survey on Causal Inference <s> Datasets for Section 3. <s> Estimating what would be an individual's potential response to varying levels of exposure to a treatment is of high practical relevance for several important fields, such as healthcare, economics and public policy. However, existing methods for learning to estimate counterfactual outcomes from observational data are either focused on estimating average dose-response curves, or limited to settings with only two treatments that do not have an associated dosage parameter. Here, we present a novel machine-learning approach towards learning counterfactual representations for estimating individual dose-response curves for any number of treatments with continuous dosage parameters with neural networks. Building on the established potential outcomes framework, we introduce performance metrics, model selection criteria, model architectures, and open benchmarks for estimating individual dose-response curves. Our experiments show that the methods developed in this work set a new state-of-the-art in estimating individual dose-response. <s> BIB020
Because the counterfactual outcome can never be observed, it's hard to find the dataset that perfectly satisfies the requirements of the experiment that it is an observational dataset with the ground truth ATE (or ITE) available. The datasets used in the literature are often semi-synthetic datasets. Some datasets, such as IHDP dataset, are obtained from the randomized dataset by generating their observed outcome according to a certain generation process and removing a biased subset to mimic the selection bias in the observational dataset. Some datasets, such as Jobs dataset, combine the randomized dataset and the observational control dataset together to create the selection bias. The details of the available benchmark datasets are in the following. IHDP. This dataset is a commonly adopted benchmark dataset. This dataset is generated based on the randomized controlled experiment conducted by Infant Health and Development Program BIB001 , whose targets are low-birthweight, premature infants. The pre-treatment covariates are 25 variables measuring the aspects about the children and their mothers, such as birth weight, head circumference, neonatal health index, prenatal care, mother's age, education, drugs, alcohol, etc. In the treated group, the infants are provided with both intensive high-quality childcare and specialist home visits BIB004 . The outcome is the infants' cognitive test score and can be simulated through the NPCI package 1 . Besides, a biased subset of the treated group is required to be removed to simulate the selection bias. An example of IHDP dataset whose outcome is simulated by the setting "A" of NPCI package can be downloaded from http://www.mit.edu/~fredrikj/files/ihdp_100.tar.gz. Jobs. The jobs datasets used in the observational study  BIB002 BIB009 is the combination of Lalonde experiment data and the PSID comparison group. Both Lalonde and PSID datasets can be downloaded from NBER website 2 . The pre-treatment covariates are 8 variables such as age, education, ethnicity, as well as earnings in 1974 and 1975. The people in the treated group take part in the job training while in the control group are not. The outcome is employment status. Twins. Twins dataset is first introduced in BIB010 and is adopt by various observational studies BIB010 BIB015 BIB016 . Twins dataset is constructed on the data of twins birth in the USA between 1989-1991 BIB003 BIB011 . In BIB010 , the twins whose gender is the same and weight is less than 2000д are selected into records. For each twin pair, there are in total 40 pre-treatment covariates measuring the pregnancy, twins birth and the parents, such as the number of gestation weeks before birth, the quality of care during pregnancy, pregnancy risk factors (Anemia, alcohol use, tobacco use, etc.), adequacy of care, residence and so on. The outcome is the one-year mortality. The treatment is being the heavier one in the twins, and the outcome is the one-year mortality. In twins dataset, both treated (the heavier one in the twin) and control (the lighter one in the twin) outcomes are observed. The treatment assignment usually defined by the users to simulate the selection bias. For example, in BIB010 BIB016 , the selection bias is created by the following procedure: ACIC datasets. Starting from 2016, every year, the Atlantic Causal Inference Conference holds the causal inference data analysis challenge, which provides some datasets targeting different causal inference problems. 2016 Challenge: The goal of ACIC 2016 Challenge is to better understand which approaches to causal inference perform well in particular observational study settings BIB012 . The datasets contain 77 datasets with varying degrees of non-linearity, sparsity, correlation between treatment assignment and outcome, non-linearity of treatment effect, overlapping. The covariates are real-world data from the Infant Health and Development Program dataset BIB001 , which consists of 58 variables. The treatment, factual outcome, and counterfactual outcome are all generated by simulation, and the selection bias is created by removing treated children with non-white moms. The whole datasets can be downloaded from https://drive.google.com/file/d/0B7pG5PPgj6A3N09ibmFwNWE1djA/view, and the summarization of this year's challenge is in BIB013 . 2017 Challenge: ACIC 2017 challenge focused on the estimation and inference for conditional average treatment effects (CATEs) in the presence of targeted selection. Targeted selection means the likelihood that an individual receives treatment is a function of the expected response of that individual if left untreated, which leads to strong confounding BIB019 . The same as the previous year's challenge, the covariates are from Infant Health and Development Program dataset BIB001 , but only 8 variables are used. The outcomes and the treatment assignments are generated according to 32 distinct, fixed, data generating processes representing four different types of errors. For every data generating process, 250 independent replicate data sets were produced, and overall, there are a total of 8, 000 data sets. 2018 Challenge: The ACIC 2018 challenge has two different tasks focusing on two sub-challenges: censoring and scaling. Censoring means some of the samples may not have observed outcomes. Therefore, the dataset used by censoring challenges contains missing outcome values for some of the samples. The dataset for scaling challenge contains 48 datasets whose data sizes, and they are not censored. The details of the above datasets are available at https://www.synapse.org/#!Synapse:syn11294478/wiki/486304 2019 Challenge: This challenge focuses on estimating the ATE on the quasi real-world dataset with low dimensional data and high dimensional data BIB003 . The datasets for this challenge contain several datasets with different variables size and record size, and the R code for data generation is available at https://drive.google. com/file/d/1Qqgmb3R9Vt9KTx6t8i_5IbFenylsPfrK/view. IBM causal inference benchmark. This dataset is created in BIB017 and is available at https://github.com/IBM-HRL-MLHLS/IBM-Causal-Inference-Benchmarking-Framework. This dataset uses the cohort of 100K samples in Linked Births and Infant Deaths Database (LBIDD) BIB007 as the fundamental set of covariates. The treatment, factual outcome, and the counterfactual outcome are generated by simulation. BlogCatalog. This dataset is used for causal inference with networked observational data  . It is a social blogger network. A blogger is one observation. The bloggers are connected by some social relationships in this dataset. The features are bag-of-words representations of keywords in bloggers' descriptions. The outcomes are the opinions of readers on each blogger. A blogger belongs to the treated group (control group) if her blogs are read more on mobile devices (desktops). Flickr. This dataset includes networked observational data in  . Flickr is a photo-sharing platform and social network where users upload photos for others to see. In this dataset, the users with Flickr account are observations, and the users are connected by some social relationships. The features of each user are the tags of interest. The outcomes and treatment assignment are the same as BlogCatalog. News. The News benchmark includes 5000 randomly sampled news articles from the NY Times corpus. It contains the data on the opinion of media consumers on news items and was originally introduced as a benchmark for counterfactual inference in the setting with two treatment options BIB008 . It can be extended to multiple treatments with associated dosage parameters BIB020 . The details can be found in https://archive.ics.uci. edu/ml/datasets/bag+of+words. MVICU. The Mechanical Ventilation in the Intensive Care Unit (MVICU) benchmark is used to estimate individual dose-response curves for a couple of treatments with an associated dosage parameter BIB020 . This dataset includes patients' responses to different configurations of mechanical ventilation in the intensive care unit. The data was sourced from the publicly available MIMIC III database which documents a diverse and very large population of ICU patient stays and contains comprehensive and detailed clinical data. BIB005 . TCGA. The Cancer Genome Atlas (TCGA) is the world's largest and richest collection of genomic data. This dataset is used to estimate individual dose-response curves for a couple of treatments with an associated dosage parameter BIB020 . The TCGA project collected gene expression data from various types of cancers in 9659 individuals BIB006 . The treatment options are medication, chemotherapy and surgery. The outcome is the risk of cancer recurrence after receiving the treatment. TCGA data (controlled access and open access data) are available via the Genomic Data Commons (GDC) https://gdc.cancer.gov/. Saccharomyces cerevisiae (yeast) cell cycle gene expression dataset. This is one time series dataset. A time series with the length T = 57 was created by combining four short time series that were measured in different microarray experiments  . THE. The Tennessee Student/Teacher Achievement Ratio (STAR) experiment is a four-year longitudinal classsize study funded by the Tennessee General Assembly and conducted by the State Department of Education to measure the influence of class size (small class, regular class and regular-with-aide class) on student achievement tests and non-achievement measures  . Because this is one randomized controlled experiment, CATE estimates are unbiased due to unconfoundedness. Confounders are artificially introduced by selectively removing a biased subset of samples BIB018 . FERTIL2. This dataset aims to study the impact of more than or exactly 7 years of education for a woman on the number of children in the family  . Several observed confounders are included in the dataset, such as age, whether the family has a TV, whether the woman lives in the city. The instrumental variable is a binary indicator of whether the woman was born in the first half of the year. This dataset is used for research about instrumental variables BIB014 .