We develop new algorithms for estimating heterogeneous treatment effects, combining recent developments in transfer learning for neural networks with insights from the causal inference literature. By taking advantage of transfer learning, we are able to efficiently use different data sources that are related to the same underlying causal mechanisms. We compare our algorithms with those in the extant literature using extensive simulation studies based on large-scale voter persuasion experiments and the MNIST database. Our methods can perform an order of magnitude better than existing benchmarks while using a fraction of the data. The rise of massive datasets that provide fine-grained information about human beings and their behavior provides unprecedented opportunities for evaluating the effectiveness of treatments. Researchers want to exploit these large and heterogeneous datasets, and they often seek to estimate how well a given treatment works for individuals conditioning on their observed covariates. This problem is important in medicine (where it is sometimes called personalized medicine) (Henderson et al., 2016; Powers et al., 2018) , digital experiments (Taddy et al., 2016) , economics (Athey and Imbens, 2016) , political science (Green and Kern, 2012) , statistics (Tian et al., 2014) , and many other fields. Although a large number of articles are being written on this topic, many outstanding questions remain. We present the first paper that applies transfer learning to this problem.In the simplest case, treatment effects are estimated by splitting a training set into a treatment and a control group. The treatment group receives the treatment, while the control group does not. The outcomes in those groups are then used to construct an estimator for the Conditional Average Treatment Effect (CATE), which is defined as the expected outcome under treatment minus the expected outcome under control given a particular feature vector (Athey and Imbens, 2015) . This is a challenging task because, for every unit, we either observe its outcome under treatment or control, but never both. Assumptions, such as the random assignment of treatment and additional regularity conditions, are needed to make progress. Even with these assumptions, the resulting estimates are often noisy and unstable because the CATE is a vector parameter. Recent research has shown that it is important to use estimators which consider both treatment groups simultaneously (Künzel et al., 2017; Wager and Athey, 2017; Nie and Wager, 2017; Hill, 2011) . Unfortunately, these recent advances are often still insufficient to train robust CATE estimators because of the large sample sizes required when the number of covariates is not small.In this paper, we show how these difficulties in estimating the CATE can sometimes be overcome through the use of transfer learning. In particular, we provide several strategies for utilizing ancillary datasets that are related to the causal mechanism under investigation. Examples of such datasets include observations from: experiments in different locations on different populations, different treatment arms, different outcomes, and non-experimental observational studies. We show that, by transferring information from these ancillary datasets, CATE estimators can converge to better solutions with fewer samples. This is particularly important for CATE estimation, as the cost of collecting additional data is quite high and often requires real-world data collection. Our contributions are as follows:1. We introduce the new problem of transfer learning for estimating heterogeneous treatment effects.2. MLRW Transfer for CATE Estimation adapts the idea of meta-learning regression weights (MLRW) to CATE estimation. By using a learned initialization, regression problems can be optimized much more quickly than with random initializations. Though a variety of MLRW algorithms exist, it is not immediately obvious how one should use these methods for CATE estimation. The principal difficulty is that CATE estimation requires the simultaneous estimation of outcomes under both treatment and control, but we only observe one of the outcomes for any individual unit. Most MLRW transfer methods optimize on a per-task basis to estimate a single quantity. We show that one can overcome this problem with clever use of the Reptile algorithm (Nichol et al., 2018) .3. We provide several additional methods for transfer learning for CATE estimation: warm start, frozen-features, multi-head, and joint training.4. We apply our methods to difficult data problems and show that they perform better than existing benchmarks. We reanalyze a set of large field experiments that evaluate the effect of a mailer on voter turnout in the 2014 U.S. midterm elections (Gerber et al., 2017) . This includes 17 experiments with 1.96 million individuals in total. We also simulate several randomized controlled trials using image data of handwritten digits found in the MNIST database (LeCun, 1998) . We show that our methods, MLRW in particular, obtain better than state-of-the-art performance in estimating CATE, and that they require far fewer observations than extant methods.
Transfer learning for estimating causal effects using neural networks. Develops algorithms to estimate conditional average treatment effect by auxiliary dataset in different environments, both with and without base learner. The authors propose methods to address a novel task of transfer learning for estimating the CATE function, and evaluate them using a synthetic setting and a real-world experimental dataset. Using neural network regression and comparing transfer learning frameworks to estimate a conditional average treatment effect under string ignorability assumptions