Causal inference is the task of drawing a conclusion about a causal connection based on the conditions of the occurrence of an effect.
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In addition to efficient statistical estimators of a treatment's effect, successful application of causal inference requires specifying assumptions about the mechanisms underlying observed data and testing whether they are valid, and to what extent.
CausalML is a Python implementation of algorithms related to causal inference and machine learning.
Fortunately, this regularization bias can be removed by solving auxiliary prediction problems via ML tools.
These feature attributions convey how important a feature is to changing the classification outcome of a model, especially on whether a subset of features is necessary and/or sufficient for that change, which feature attribution methods are unable to provide.
Today's scene graph generation (SGG) task is still far from practical, mainly due to the severe training bias, e. g., collapsing diverse "human walk on / sit on / lay on beach" into "human on beach".
Ranked #1 on Scene Graph Generation on Visual Genome
To quantify such differences, we propose a (pre-) distance between DAGs, the structural intervention distance (SID).
On one hand, it has a harmful causal effect that misleads the tail prediction biased towards the head.
We next utilize the augmented form to develop a masked structure learning method that can be efficiently trained using gradient-based optimization methods, by leveraging a smooth characterization on acyclicity and the Gumbel-Softmax approach to approximate the binary adjacency matrix.