We present two online causal structure learning algorithms which can track changes in a causal structure and process data in a dynamic real-time manner. Standard causal structure learning algorithms assume that causal structure does not change during the data collection process, but in real-world scenarios, it does often change. Therefore, it is inappropriate to handle such changes with existing batch-learning approaches, and instead, a structure should be learned in an online manner. The online causal structure learning algorithms we present here can revise correlation values without reprocessing the entire dataset and use an existing model to avoid relearning the causal links in the prior model, which still fit data. Proposed algorithms are tested on synthetic and real-world datasets, the latter being a seasonally adjusted commodity price index dataset for the U.S. The online causal structure learning algorithms outperformed standard FCI by a large margin in learning the changed causal structure correctly and efficiently when latent variables were present.
@cite_15: Changepoints are abrupt variations in the generative parameters of a data sequence. Online detection of changepoints is useful in modelling and prediction of time series in application areas such as finance, biometrics, and robotics. While frequentist methods have yielded online filtering and prediction techniques, most Bayesian papers have focused on the retrospective segmentation problem. Here we examine the case where the model parameters before and after the changepoint are independent and we derive an online algorithm for exact inference of the most recent changepoint. We compute the probability distribution of the length of the current run,'' or time since the last changepoint, using a simple message-passing algorithm. Our implementation is highly modular so that the algorithm may be applied to a variety of types of data. We illustrate this modularity by demonstrating the algorithm on three different real-world data sets.
The standard TDL algorithm provides a dynamic estimate of a univariate random variable using a simple update rule. In this update, the error in the current estimate is updated with a learning rate coefficient. Therefore, the static learning rate plays an important role and controls how quickly or slowly a model learns a problem. If it is chosen too small, the TDL algorithm converges slowly. If it is chosen too large, the algorithm will be so sensitive even when the environment is stable. TDL algorithm can detect slow change but not high stability or dramatic changes. That feature is essential for causal structure learning as causal structures often have non-deterministic connections. In contrast, CPD algorithms are useful for dramatic changes that indicate breaks between periods of stability @cite_15 . CPD algorithms must store large parts of input data. These algorithms assume that the model only has a dramatically changing environment separated by periods of stability. Both algorithm types have not been applied to tracking causal structure. To do so, they need the necessary modifications.