Stock trend forecasting, which aims to predict future fluctuations of stock price, has garnered significant attention in recent years. However, it is quite hard to train a model that can consistently and accurately forecasts the future movements of stocks. Since stock data is influenced by various unobservable factors, such as market sentiment and industry conditions, its distribution is unstable and can change over time. As a result, models trained on stock data often exhibit poor performance in prediction. Causal inference is an effective method that has been widely used to address distribution shift. However, how to conduct causal inference for stock trend forecasting still remains under-explored. To this end, we propose CISTF, a method based on causal inference to explore the causal dependencies between the input historical data and the future trend of stocks. We first construct a causal graph for the stock trend forecasting task, in which we use confounders to represent unobservable factors that affect both the input data and the future trend of stocks. Such time-varying confounders contribute to the distribution drift in the stock data. Then we use front-door adjustment, an intervention strategie that allow we to intervene the input data, to eliminate the spurious correlations brought by the unobserved confounders. Additionally, we develop a deep architecture to implement front-door adjustment, which consists of a feature extractor, a mediator estimation module and a conditional probability estimation module. We evaluate the effectiveness of CISTF on real-world stock data, and experiments on three public stock datasets demonstrate that our model achieves state-of-the-art performance.

Time series forecasting, aiming to learn models from historical data and predict future values in time series, is a fundamental research topic in machine learning. However, few efforts have been devoted to addressing the confounding effects in time series data, e.g., the historical data are affected by some hidden surrounding factors (i.e., confounders), leading to biased forecasting models for future data. This paper presents a causal intervention approach to eliminate the bias that is raised by some hidden confounders. By using a causal graph, we illustrate why hidden confounders can bring bias in time series forecasting and how to tackle it. We implement causal intervention by a deep architecture that consists of two modules, a Confounders Estimation module to estimate the hidden confounders and a Debiasing module to eliminate the confounding bias in the forecasting model via sampling on confounders. We conduct comprehensive evaluations on various time series datasets. The experiment results indicate that the proposed method can reduce the negative confounding effects in time series data, and it achieves superior gains over state-of-the-art baselines for time series forecasting.

Multi-hop QA requires reasoning over multiple supporting facts to answer the question.However, the existing QA models always rely on shortcuts, e.g., providing the true answer by only one fact, rather than multi-hop reasoning, which is referred to as disconnected reasoning problem.To alleviate this issue, we propose a novel counterfactual multihop QA, a causaleffect approach that enables to reduce the disconnected reasoning.It builds upon explicitly modeling of causality: 1) the direct causal effects of disconnected reasoning and 2) the causal effect of true multi-hop reasoning from the total causal effect.With the causal graph, a counterfactual inference is proposed to disentangle the disconnected reasoning from the total causal effect, which provides us a new perspective and technology to learn a QA model that exploits the true multi-hop reasoning instead of shortcuts.Extensive experiments have been conducted on the benchmark HotpotQA dataset, which demonstrate that the proposed method can achieve notable improvement on reducing disconnected reasoning.For example, our method achieves 5.8% higher points of its Supp s score on HotpotQA through true multihop reasoning.The code is available at https://github.com/guowzh/CFMQA.