52 research outputs found
A Logical Characterization of Constraint-Based Causal Discovery
We present a novel approach to constraint-based causal discovery, that takes
the form of straightforward logical inference, applied to a list of simple,
logical statements about causal relations that are derived directly from
observed (in)dependencies. It is both sound and complete, in the sense that all
invariant features of the corresponding partial ancestral graph (PAG) are
identified, even in the presence of latent variables and selection bias. The
approach shows that every identifiable causal relation corresponds to one of
just two fundamental forms. More importantly, as the basic building blocks of
the method do not rely on the detailed (graphical) structure of the
corresponding PAG, it opens up a range of new opportunities, including more
robust inference, detailed accountability, and application to large models
Constraint-Based Causal Discovery using Partial Ancestral Graphs in the presence of Cycles
While feedback loops are known to play important roles in many complex
systems, their existence is ignored in a large part of the causal discovery
literature, as systems are typically assumed to be acyclic from the outset.
When applying causal discovery algorithms designed for the acyclic setting on
data generated by a system that involves feedback, one would not expect to
obtain correct results. In this work, we show that---surprisingly---the output
of the Fast Causal Inference (FCI) algorithm is correct if it is applied to
observational data generated by a system that involves feedback. More
specifically, we prove that for observational data generated by a simple and
-faithful Structural Causal Model (SCM), FCI is sound and complete, and
can be used to consistently estimate (i) the presence and absence of causal
relations, (ii) the presence and absence of direct causal relations, (iii) the
absence of confounders, and (iv) the absence of specific cycles in the causal
graph of the SCM. We extend these results to constraint-based causal discovery
algorithms that exploit certain forms of background knowledge, including the
causally sufficient setting (e.g., the PC algorithm) and the Joint Causal
Inference setting (e.g., the FCI-JCI algorithm).Comment: Major revision. To appear in Proceedings of the 36 th Conference on
Uncertainty in Artificial Intelligence (UAI), PMLR volume 124, 202
Ancestral Causal Inference
Constraint-based causal discovery from limited data is a notoriously
difficult challenge due to the many borderline independence test decisions.
Several approaches to improve the reliability of the predictions by exploiting
redundancy in the independence information have been proposed recently. Though
promising, existing approaches can still be greatly improved in terms of
accuracy and scalability. We present a novel method that reduces the
combinatorial explosion of the search space by using a more coarse-grained
representation of causal information, drastically reducing computation time.
Additionally, we propose a method to score causal predictions based on their
confidence. Crucially, our implementation also allows one to easily combine
observational and interventional data and to incorporate various types of
available background knowledge. We prove soundness and asymptotic consistency
of our method and demonstrate that it can outperform the state-of-the-art on
synthetic data, achieving a speedup of several orders of magnitude. We
illustrate its practical feasibility by applying it on a challenging protein
data set.Comment: In Proceedings of Advances in Neural Information Processing Systems
29 (NIPS 2016
Establishing Markov Equivalence in Cyclic Directed Graphs
We present a new, efficient procedure to establish Markov equivalence between
directed graphs that may or may not contain cycles under the
\textit{d}-separation criterion. It is based on the Cyclic Equivalence Theorem
(CET) in the seminal works on cyclic models by Thomas Richardson in the mid
'90s, but now rephrased from an ancestral perspective. The resulting
characterization leads to a procedure for establishing Markov equivalence
between graphs that no longer requires tests for d-separation, leading to a
significantly reduced algorithmic complexity. The conceptually simplified
characterization may help to reinvigorate theoretical research towards sound
and complete cyclic discovery in the presence of latent confounders. This
version includes a correction to rule (iv) in Theorem 1, and the subsequent
adjustment in part 2 of Algorithm 2.Comment: Correction to original version published at UAI-2023. Includes
additional experimental results and extended proof details in supplemen
Towards a Benchmark for Scientific Understanding in Humans and Machines
Scientific understanding is a fundamental goal of science, allowing us to
explain the world. There is currently no good way to measure the scientific
understanding of agents, whether these be humans or Artificial Intelligence
systems. Without a clear benchmark, it is challenging to evaluate and compare
different levels of and approaches to scientific understanding. In this
Roadmap, we propose a framework to create a benchmark for scientific
understanding, utilizing tools from philosophy of science. We adopt a
behavioral notion according to which genuine understanding should be recognized
as an ability to perform certain tasks. We extend this notion by considering a
set of questions that can gauge different levels of scientific understanding,
covering information retrieval, the capability to arrange information to
produce an explanation, and the ability to infer how things would be different
under different circumstances. The Scientific Understanding Benchmark (SUB),
which is formed by a set of these tests, allows for the evaluation and
comparison of different approaches. Benchmarking plays a crucial role in
establishing trust, ensuring quality control, and providing a basis for
performance evaluation. By aligning machine and human scientific understanding
we can improve their utility, ultimately advancing scientific understanding and
helping to discover new insights within machines
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