  Causal video question answering (QA) has garnered increasing interest, yet
existing datasets often lack depth in causal reasoning. To address this gap, we
capitalize on the unique properties of cartoons and construct CausalChaos!, a
novel, challenging causal Why-QA dataset built upon the iconic "Tom and Jerry"
cartoon series. Cartoons use the principles of animation that allow animators
to create expressive, unambiguous causal relationships between events to form a
coherent storyline. Utilizing these properties, along with thought-provoking
questions and multi-level answers (answer and detailed causal explanation), our
questions involve causal chains that interconnect multiple dynamic interactions
between characters and visual scenes. These factors demand models to solve more
challenging, yet well-defined causal relationships. We also introduce hard
incorrect answer mining, including a causally confusing version that is even
more challenging. While models perform well, there is much room for
improvement, especially, on open-ended answers. We identify more
advanced/explicit causal relationship modeling & joint modeling of vision and
language as the immediate areas for future efforts to focus upon. Along with
the other complementary datasets, our new challenging dataset will pave the way
for these developments in the field.
