This paper focuses on the expected difference in borrower's repayment when there is a change in the lender's credit decisions. Classical estimators overlook the confounding effects and hence the estimation error can be magnificent. As such, we propose another approach to construct the estimators such that the error can be greatly reduced. The proposed estimators are shown to be unbiased, consistent, and robust through a combination of theoretical analysis and numerical testing. Moreover, we compare the power of estimating the causal quantities between the classical estimators and the proposed estimators. The comparison is tested across a wide range of models, including linear regression models, tree-based models, and neural network-based models, under different simulated datasets that exhibit different levels of causality, different degrees of nonlinearity, and different distributional properties. Most importantly, we apply our approaches to a large observational dataset provided by a global technology firm that operates in both the e-commerce and the lending business. We find that the relative reduction of estimation error is strikingly substantial if the causal effects are accounted for correctly.

The essence of multivariate sequential learning is all about how to extract dependencies in data. These data sets, such as hourly medical records in intensive care units and multi-frequency phonetic time series, often time exhibit not only strong serial dependencies in the individual components (the "marginal" memory) but also non-negligible memories in the cross-sectional dependencies (the "joint" memory). Because of the multivariate complexity in the evolution of the joint distribution that underlies the data generating process, we take a data-driven approach and construct a novel recurrent network architecture, termed Memory-Gated Recurrent Networks (mGRN), with gates explicitly regulating two distinct types of memories: the marginal memory and the joint memory. Through a combination of comprehensive simulation studies and empirical experiments on a range of public datasets, we show that our proposed mGRN architecture consistently outperforms state-of-the-art architectures targeting multivariate time series.

Many practical problems can be formulated as graph-based semi-supervised classification problems. For example, online finance anti-fraud. Recently, many researchers attempt using deep learning methods to solve such problems. In this paper, we propose a novel neural network architecture to perform semi-supervised classification on graph-structured data. We improve the graph convolutional network (GCN) by replacing the graph convolution matrix with auto-encoder module. The proposed neural network is trained by a multi-task objective function. Except the classification task, we train the auto-encoder module to reconstruct the graph convolution matrix. It can be seen as an adaptive spectral convolution on graph. It can increase the depth of neural network without causing over-smooth effect. Additionally, the introduction of reconstruction task can mitigate the cold-start problem. Even the graph topological structure is extreme sparse, our method can learn expressive latent features for vertices. The experimental results show that our method can achieve the state of art performance.