Meta Learning for Causal Direction

The inaccessibility of controlled randomized trials due to inherent constraints in many fields of science has been a fundamental issue in causal inference. In this paper, we focus on distinguishing the cause from effect in the bivariate setting under limited observational data. Based on recent developments in meta learning as well as in causal inference, we introduce a novel generative model that allows distinguishing cause and effect in the small data setting. Using a learnt task variable that contains distributional information of each dataset, we propose an end-to-end algorithm that makes use of similar training datasets at test time. We demonstrate our method on various synthetic as well as real-world data and show that it is able to maintain high accuracy in detecting directions across varying dataset sizes

Causal Discovery Under Local Privacy

Differential privacy is a widely adopted framework designed to safeguard the sensitive information of data providers within a data set. It is based on the application of controlled noise at the interface between the server that stores and processes the data, and the data consumers. Local differential privacy is a variant that allows data providers to apply the privatization mechanism themselves on their data individually. Therefore it provides protection also in contexts in which the server, or even the data collector, cannot be trusted. The introduction of noise, however, inevitably affects the utility of the data, particularly by distorting the correlations between individual data components. This distortion can prove detrimental to tasks such as causal discovery. In this paper, we consider various well-known locally differentially private mechanisms and compare the trade-off between the privacy they provide, and the accuracy of the causal structure produced by algorithms for causal learning when applied to data obfuscated by these mechanisms. Our analysis yields valuable insights for selecting appropriate local differentially private protocols for causal discovery tasks. We foresee that our findings will aid researchers and practitioners in conducting locally private causal discovery

Automating inventory composition management for bulk purchasing cloud brokerage strategy

Cloud providers offer end-users various pricing schemes to allow them to tailor VMs to their needs, e.g., a pay-as-you-go billing scheme, called on-demand, and a discounted contract scheme, called reserved instances. This work presents a cloud broker that offers users both the flexibility of on-demand instances and some discounts found in reserved instances. The broker employs a buy-low-and-sell-high strategy that places user requests into a resource pool of pre-purchased discounted cloud resources. A key challenge to buy-in-bulk-sell-individually cloud broker business models is to estimate user requests accurately and then optimise the stock level accordingly. Given the complexity and variety of the cloud computing market space, the number of the regression model and inherently optimisation search space can be intricate. In this thesis, we propose two solutions to the problem. The first solution is a risk-based decision model. The broker takes a risk-oriented approach to dynamically adjust the resource pool by analysing user request time series data. This approach does not require a training process which is useful at processing the large data stream. The broker is evaluated with high-frequency real cloud datasets from Alibaba. The results show that the overall profit of the broker is closely related to the optimal case. Additionally, the risk factors work as intended. The system hires more reserved instances when it can afford while leaning to the on-demand otherwise. We can also conclude that there is a correlation between the risk factors and the profit. On the other hand, the risk factor possesses some limitations, i.e. manual risk configuration, limited broker setting. Secondly, we propose a broker system that utilises the concept of causal discovery. From the risk-based solution, we can see that if there are parameters correlated with the profit, then by adjusting those parameters, we can manipulate the profit. We infer a function mapping from the extracted key entities of broker data to an objective of a broker, e.g. profit. The technique is similar to the additive noise model, causal discovery method. These functions are assumed to describe an actual underlying behaviour of the profit with respect to the parameters. Similar to the risk-based, we use the Alibaba trace data to simulate long term user requests. Our results show that the system can infer the underlying interaction model between variables unlock the profit model behaviour of the broker system

Conditional distribution variability measures for causality detection

In this paper we derive variability measures for the conditional probability distributions of a pair of random variables, and we study its application in the inference of causal-effect relationships. We also study the combination of the proposed measures with standard statistical measures in the framework of the ChaLearn cause-effect pair challenge. The developed model obtains an AUC score of 0.82 on the final test database and ranked second in the challenge.Peer ReviewedPostprint (published version

Conditional distribution variability measures for causality detection

In this paper we derive variability measures for the conditional probability distributions of a pair of random variables, and we study its application in the inference of causal-effect relationships. We also study the combination of the proposed measures with standard statistical measures in the framework of the ChaLearn cause-effect pair challenge. The developed model obtains an AUC score of 0.82 on the final test database and ranked second in the challenge.Peer Reviewe