Learning why things change: The Difference-Based Causality Learner

In this paper, we present the Difference-Based Causality Learner (DBCL), an algorithm for learning a class of discrete-time dynamic models that represents all causation across time by means of difference equations driving change in a system. We motivate this representation with real-world mechanical systems and prove DBCL's correctness for learning structure from time series data, an endeavour that is complicated by the existence of latent derivatives that have to be detected. We also prove that, under common assumptions for causal discovery, DBCL will identify the presence or absence of feedback loops, making the model more useful for predicting the effects of manipulating variables when the system is in equilibrium. We argue analytically and show empirically the advantages of DBCL over vector autoregression (VAR) and Granger causality models as well as modified forms of Bayesian and constraintbased structure discovery algorithms. Finally, we show that our algorithm can discover causal directions of alpha rhythms in human brains from EEG data

Explorations into African Land Resource Ecology: On the chemistry between soils, plants and fertilizers

Keyzer, M.A. [Promotor]Rabbinge, R. [Promotor

Learning causal models that make correct manipulation predictions with time series data

One of the fundamental purposes of causal models is using them to predict the effects of manipulating various components of a system. It has been argued by Dash (2005, 2003) that the Do operator will fail when applied to an equilibrium model, unless the underlying dynamic system obeys what he calls Equilibration-Manipulation Commutability. Unfortunately, this fact renders most existing causal discovery algorithms unreliable for reasoning about manipulations. Motivated by this caveat, in this paper we present a novel approach to causal discovery of dynamic models from time series. The approach uses a representation of dynamic causal models motivated by Iwasaki and Simon (1994), which asserts that all “causation across time" occurs because a variable’s derivative has been affected instantaneously. We present an algorithm that exploits this representation within a constraint-based learning framework by numerically calculating derivatives and learning instantaneous relationships. We argue that due to numerical errors in higher order derivatives, care must be taken when learning causal structure, but we show that the Iwasaki-Simon representation reduces the search space considerably, allowing us to forego calculating many high-order derivatives. In order for our algorithm to discover the dynamic model, it is necessary that the time-scale of the data is much finer than any temporal process of the system. Finally, we show that our approach can correctly recover the structure of a fairly complex dynamic system, and can predict the effect of manipulations accurately when a manipulation does not cause an instability. To our knowledge, this is the first causal discovery algorithm that has demonstrated that it can correctly predict the effects of manipulations for a system that does not obey the EMC condition

Recent historical climate change and its effect on land use in the eastern part of West Africa

There are indications that low rainfall, drought periods and famine become more frequent in West Africa. This may in part be a rather early expression of the effect of global warming but it is very likely that local factors such as drastic changes in land cover due to expanded cultivated area, as required by a growing population, play an important role. Before studying the causative mechanisms of climate change, it first needs to be established that climate did indeed change significantly, and if so, its nature, extent and magnitude have to be quantified. To this effect time series of annual rainfall (1950-1992) for 42 synoptic climate stations in eastern West Africa, covering 5 countries, were analyzed. The data were subjected to several statistical tests for the entire time series and parts thereof. The outcomes were interpolated in a GIS environment to assess the spatial pattern of change. The time series and spatial analysis reveals that climate change is indeed significant in the northern part of the study area and that the degree of change shows a spatial pattern that can be related to the weather system in combination with topography. A remarkable feature is that the change in rainfall is not a gradual one, but consists of a trend break with zero trend before and after the break. This is unexpected because it might imply that causal factors have to be sought under those that do not change gradually. The year of occurrence of the break is around 1970 but varies in time, again according to a geographic pattern. The reduction of rainfall shortens the length of the growing period (LGP) and has a considerable impact on potential crop yields and their variability. The paper shows the serious implications of recent historical climate change for land use in the semi-arid region of West Africa

The Outcome of Relationships between Students, Parents, and School Personnel while Desegregating Schools within Mississippi 1950s to 1970s

Between 1950 and the end of the 1970s, schools in Mississippi went through the formal process of legal desegregation. The oral histories of a select few of these students live on to explain the hardships Black students faced in segregated classrooms and integrated classrooms alike. Students, parents and teachers who integrated the education system were forever changed by community activism, local legislature, and personal interactions. This paper will examine and compare how different teacher and student relationships impacted the various futures that students went on to live, shaping their decisions on education and on life

The burden of neurosarcoidosis and small fiber neuropathy associated symptoms

Sarcoidosis is a rare inflammatory disease that can affect many organs and may cause a variety of symptoms. Nervous system involvement, also known as neurosarcoidosis, occurs in 10-15%. The most common manifestations in the Netherlands were meningitis and cranial nerve dysfunction. In a European survey, fatigue and reduced energy levels were reported by almost all sarcoidosis patients (90%), followed by pain, pulmonary symptoms, memory, concentration and sleeping problems. Neurosarcoidosis patients reported even more cognitive failure than general sarcoidosis patients (56% vs 35%). In contrast to other manifestations, the majority of neurosarcoidosis patients (> 90%) require treatment. Hence having sarcoidosis affects not only the quality of life of the patient, but also that of the partners, it is important to involve them in the management as well