Kurt Butler's research while affiliated with Stony Brook University and other places
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Publications (11)
Counterfactual reasoning allows us to explore hypothetical scenarios in order to explain the impacts of our decisions. However, addressing such inquires is impossible without establishing the appropriate mathematical framework. In this work, we introduce the problem of counterfactual reasoning in the context of vector autoregressive (VAR) processes...
We introduce
Dagma-DCE
, an interpretable and model-agnostic scheme for differentiable causal discovery. Current non- or over-parametric methods in differentiable causal discovery use opaque proxies of “independence” to justify the inclusion or exclusion of a causal relationship. We show theoretically and empirically that these proxies may be arb...
In the study of causality, we often seek not only to detect the presence of cause-effect relationships, but also to characterize how multiple causes combine to produce an effect. When the response to a change in one of the causes depends on the state of another cause, we say that there is an interaction or joint causation between the multiple cause...
We are delighted to present you the Proceedings of the 2022 CNS meeting. The CNS meeting encourages approaches that combine theoretical, computational, and experimental work in the neurosciences, and provides an opportunity for participants to share their views. The abstracts corresponding to speakers' talks and posters are what you find collected...
Convergent cross mapping is a principled causal discovery technique for signals, but its efficacy depends on a number of assumptions about the systems that generated the signals. In this work, we present a self-contained introduction to the theory of causality in state-spaces, Takens' theorem, and cross maps, and we propose conditions to check if a...
The ability to quantify the strength of an interaction between events represented by random variables is important in many applications such as medicine and environmental science. We present the problem of measuring the strength of a causal interaction, starting from the linear perspective and generalizing to a nonlinear measure of causal influence...
Citations
... If we perform an intervention at time t and attempt to predict its effect at time t + k, we need to propagate the changes through the graph in Figure 1, that is, through each intermediate time step. When Q > 1, the path in the graph from x t to x t+k is not unique, and the total sensitivity of x t+k to x t is given by a sum over all paths in the graph [15], [16]. Since the VAR model is time-invariant, the total sensitivity is a matrix T k which only depends upon k. ...
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... Methods based on predictability, such as Granger causality [7], can deliver false positives when not all relevant variables are included in the model. Other methods may require strong assumptions; for example, convergent cross mapping [8] requires the existence of a dynamical attractor, which is difficult to test on small data sets [9]. In comparison, when it is possible to perform interventions, causal inference becomes considerably simpler, with several standard algorithms [3], [10]. ...
... where e i is the i-th unit vector in R D . We observe that the linear model coefficients in B 1 measure the sensitivity of x t+1 to changes in x t , and can be interpreted as a measure of the strength of the causal relationship [14]. ...
