Systems with Switching Causal Relations: A Meta-Causal Perspective

Most work on causality in machine learning assumes that causal relationships are driven by a constant underlying process. However, the flexibility of agents' actions or tipping points in the environmental process can change the qualitative dynamics of the system. As a result, new causal relationships may emerge, while existing ones change or disappear, resulting in an altered causal graph. To analyze these qualitative changes on the causal graph, we propose the concept of meta-causal states, which groups classical causal models into clusters based on equivalent qualitative behavior and consolidates specific mechanism parameterizations. We demonstrate how meta-causal states can be inferred from observed agent behavior, and discuss potential methods for disentangling these states from unlabeled data. Finally, we direct our analysis towards the application of a dynamical system, showing that meta-causal states can also emerge from inherent system dynamics, and thus constitute more than a context-dependent framework in which mechanisms emerge only as a result of external factors.

Continual Causal Abstractions

This short paper discusses continually updated causal abstractions as a potential direction of future research. The key idea is to revise the existing level of causal abstraction to a different level of detail that is both consistent with the history of observed data and more effective in solving a given task.

HANF: Hyperparameter And Neural Architecture Search in Federated Learning

Automated machine learning (AutoML) is an important step to make machine learning models being widely applied to solve real world problems. Despite numerous research advancement, machine learning methods are not fully utilized by industries mainly due to their data privacy and security regulations, high cost involved in storing and computing increasing amount of data at central location and most importantly lack of expertise. Hence, we introduce a novel framework, HANF - $\textbf{H}$yperparameter $\textbf{A}$nd $\textbf{N}$eural architecture search in $\textbf{F}$ederated learning as a step towards building an AutoML framework for data distributed across several data owner servers without any need for bringing the data to a central location. HANF jointly optimizes a neural architecture and non-architectural hyperparameters of a learning algorithm using gradient-based neural architecture search and $n$-armed bandit approach respectively in data distributed setting. We show that HANF efficiently finds the optimized neural architecture and also tunes the hyperparameters on data owner servers. Additionally, HANF can be applied in both, federated and non-federated settings. Empirically, we show that HANF converges towards well-suited architectures and non-architectural hyperparameter-sets using image-classification tasks.

Tearing Apart NOTEARS: Controlling the Graph Prediction via Variance Manipulation

Simulations are ubiquitous in machine learning. Especially in graph learning, simulations of Directed Acyclic Graphs (DAG) are being deployed for evaluating new algorithms. In the literature, it was recently argued that continuous-optimization approaches to structure discovery such as NOTEARS might be exploiting the sortability of the variable's variances in the available data due to their use of least square losses. Specifically, since structure discovery is a key problem in science and beyond, we want to be invariant to the scale being used for measuring our data (e.g. meter versus centimeter should not affect the causal direction inferred by the algorithm). In this work, we further strengthen this initial, negative empirical suggestion by both proving key results in the multivariate case and corroborating with further empirical evidence. In particular, we show that we can control the resulting graph with our targeted variance attacks, even in the case where we can only partially manipulate the variances of the data.