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knowledge could be sought. New insights could be gained |
by using the explainable ML model and the outcomes used |
to formulate hypotheses for explanations that are not yet |
known to us. These hypotheses could then be tested us - |
ing simulation software, for example, and confirmed or |
rejected. To show the potential, previous neural network |
approaches discover presumed scientific laws from obser - |
vations without providing the complete underlying prior |
knowledge to the learning method (e.g., in [ 80]). As illus - |
trated in Figure 7 , the next promising step would be to re - |
veal new hypotheses from remote sensing data. This can, |
for instance, be accomplished by searching for patterns in |
interpretations, which can be assigned to novel discoveries |
and insights when combined with domain knowledge. This |
approach may point us to previously undiscovered spatio - |
temporal input–output relationships or data biases; for ex - |
ample, interpretation tools and their resulting insights on |
how certain land-use classes are recognized could be used |
to derive improved class definitions and help with a tar - |
geted acquisition of the training data. |
2.29 –2.14 1.22 |
–0.84 0.86 –0.62Rugged Scene/ |
Natural/ClimbingMan-Made/ |
Natural Light/MetalDiving/Swimming/ |
Climbing |
Transpor ting Things |
or People/Asphalt/MatteIce/Snow/Climbing Wire/Constructing |
Building/Rusty0.2 |
0.15 |
0.1 |
0.05 |
0Urban |
Vegetation |
Rocky |
Water |
(a) (b) |
FIGURE 8. Explaining the factors behind landscape beauty in the United Kingdom [77] (a) The maps of landscape attributes contributing |
to the estimation of beauty in a series of landscape images; these maps are learned from ground-based photos and are used to predict the |
landscape attributes that are observed in single images. Such attributes are then combined end to end in a neural network that predicts |
the beauty scores. The number above the single-factor maps correspond to the contribution of the factor to landscape beauty, on average. |
(b) The land cover map of the United Kingdom, which is used for visual comparison of the single-factor maps learned automatically. |
Authorized licensed use limited to: ASU Library. Downloaded on March 07,2024 at 22:07:36 UTC from IEEE Xplore. Restrictions apply. |
99 |
JUNE 2021 IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINELEARNING CAUSE-AND-EFFECT |
RELATIONSHIPS FROM DATA |
Earth is a highly complex, dynamic, and networked system, |
where very different physical, chemical, and biological |
processes interact in several spheres and at diverse spatio - |
temporal scales. Despite the great predictive capabilities |
of current machine and deep learning methods, there |
is still little actual learning—understanding is harder |
than predicting. ML algorithms excel at fitting arbitrary, |
functional data relationships but do not have a clear |
notion of the underlying causal relationships. ML is far |
from problem understanding and even moreso from ma - |
chine intelligence (see the critical perspectives in blogs |
by Gary Marcus and Michael Jordan and the articles in |
[1] and [ 22]). |
Earth system data analysis aims to extract information |
from multivariate, nongridded data sets, where missing |
data, nonlinearities, and nonstationarities are present in |
the wild. Variables and physical processes are coupled in |
space and time, and (tele)connections can be large range, |
discontinuous, and variant in strength and intensity. Ad- |
dressing this problem will allow us to identify the right set |
of predictors, develop robust models, and avoid getting the |
right answer for the wrong reasons. The links with physics |
(direction 4 in Table 1 ) and interpretability (direction 5 in |
Table 1 ) are very strong. CAUSALITY AS THE WAY FORWARD |
Causal inference aims at discovering and explaining the |
causal structure of a system [ 20], [81], [82]. Very often, in - |
terventions in the system are not possible because of ethi - |
cal, practical, or economic reasons. Then, observational |
causal inference comes into play to extract cause-and-effect |
relationships from multivariate data sets, going beyond the |
commonly adopted correlation approach, which merely |
captures associations between variables. |
Today, the science of causal inference [ 19], [83] is advanc - |
ing fast and, under reasonable assumptions, can unravel |
causal relationships among two or more coupled variables |
even in the presence of nonlinearities and nonstationarities |
and even when time is not involved. Several rigorous algo - |
rithms have been developed in the last decade that enable |
us to make inferences across multiple variables to discover |
plausible causal relationships from observations. Causal |
inference is, of course, very relevant for scientific endeav - |
ors, but it also has impactful practical implications. For ex - |
ample, learning causal structures enables us to build more |
parsimonious and robust models: that means faster, more |
fault-tolerant, and interpretable models. |
A TAXONOMY OF CAUSAL-DISCOVERY METHODS |
Causal-discovery methods can be divided into four main |
families. First, Granger causality (GC) [ 84] is the most |
FIGURE 9. Examples of causal-inference approaches in remote sensing and the geosciences. (a) Nonlinear GC, showing a time series of |
ENSO4 (ENSO in region 4) that captures sea-surface temperature anomalies in the central equatorial Pacific, SM, and vegetation optical |
depth (VOD) to explore their causal relationships extracted using the nonlinear principal component analysis in [91]. (b) The five-day lagged |
correlation (top) and causal (bottom) maps of ENSO4 and SM interannual components using the kernel GC method in [86]. Results show |
that many of the correlations, even the highest ones (~ .),08t are not causal, thus suggesting mere spurious associations. ( continued ) |
ENSO4 |
SMreal |
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