4,218 research outputs found
Near-Optimal Multi-Perturbation Experimental Design for Causal Structure Learning
Causal structure learning is a key problem in many domains. Causal structures
can be learnt by performing experiments on the system of interest. We address
the largely unexplored problem of designing a batch of experiments that each
simultaneously intervene on multiple variables. While potentially more
informative than the commonly considered single-variable interventions,
selecting such interventions is algorithmically much more challenging, due to
the doubly-exponential combinatorial search space over sets of composite
interventions. In this paper, we develop efficient algorithms for optimizing
different objective functions quantifying the informativeness of a
budget-constrained batch of experiments. By establishing novel submodularity
properties of these objectives, we provide approximation guarantees for our
algorithms. Our algorithms empirically perform superior to both random
interventions and algorithms that only select single-variable interventions.Comment: 10 pages, 2 figures, appendix, to be published in 35th Conference on
Neural Information Processing Systems (NeurIPS 2021), fixed typos and
clarified wordin
A critical rationalist approach to organizational learning: testing the theories held by managers
The common wisdom is that Popper's critical rationalism, a method aimed at knowledge validation through falsification of theories, is inadequate for managers in organizations. This study falsifies this argument in three phases: first, it specifies the obstructers that prevent the method from being employed; second, the critical rationalist method is adapted for strategic management purposes; last, the method and the hypotheses are tested via action research. Conclusions are that once the obstructers are omitted the method is applicable and effective
LazyIter: A Fast Algorithm for Counting Markov Equivalent DAGs and Designing Experiments
The causal relationships among a set of random variables are commonly
represented by a Directed Acyclic Graph (DAG), where there is a directed edge
from variable to variable if is a direct cause of . From the
purely observational data, the true causal graph can be identified up to a
Markov Equivalence Class (MEC), which is a set of DAGs with the same
conditional independencies between the variables. The size of an MEC is a
measure of complexity for recovering the true causal graph by performing
interventions. We propose a method for efficient iteration over possible MECs
given intervention results. We utilize the proposed method for computing MEC
sizes and experiment design in active and passive learning settings. Compared
to previous work for computing the size of MEC, our proposed algorithm reduces
the time complexity by a factor of for sparse graphs where is the
number of variables in the system. Additionally, integrating our approach with
dynamic programming, we design an optimal algorithm for passive experiment
design. Experimental results show that our proposed algorithms for both
computing the size of MEC and experiment design outperform the state of the
art.Comment: 11 pages, 2 figures, ICM
Differentiable Multi-Target Causal Bayesian Experimental Design
We introduce a gradient-based approach for the problem of Bayesian optimal
experimental design to learn causal models in a batch setting -- a critical
component for causal discovery from finite data where interventions can be
costly or risky. Existing methods rely on greedy approximations to construct a
batch of experiments while using black-box methods to optimize over a single
target-state pair to intervene with. In this work, we completely dispose of the
black-box optimization techniques and greedy heuristics and instead propose a
conceptually simple end-to-end gradient-based optimization procedure to acquire
a set of optimal intervention target-state pairs. Such a procedure enables
parameterization of the design space to efficiently optimize over a batch of
multi-target-state interventions, a setting which has hitherto not been
explored due to its complexity. We demonstrate that our proposed method
outperforms baselines and existing acquisition strategies in both single-target
and multi-target settings across a number of synthetic datasets.Comment: Camera-ready version ICML 202
Counting and Sampling from Markov Equivalent DAGs Using Clique Trees
A directed acyclic graph (DAG) is the most common graphical model for
representing causal relationships among a set of variables. When restricted to
using only observational data, the structure of the ground truth DAG is
identifiable only up to Markov equivalence, based on conditional independence
relations among the variables. Therefore, the number of DAGs equivalent to the
ground truth DAG is an indicator of the causal complexity of the underlying
structure--roughly speaking, it shows how many interventions or how much
additional information is further needed to recover the underlying DAG. In this
paper, we propose a new technique for counting the number of DAGs in a Markov
equivalence class. Our approach is based on the clique tree representation of
chordal graphs. We show that in the case of bounded degree graphs, the proposed
algorithm is polynomial time. We further demonstrate that this technique can be
utilized for uniform sampling from a Markov equivalence class, which provides a
stochastic way to enumerate DAGs in the equivalence class and may be needed for
finding the best DAG or for causal inference given the equivalence class as
input. We also extend our counting and sampling method to the case where prior
knowledge about the underlying DAG is available, and present applications of
this extension in causal experiment design and estimating the causal effect of
joint interventions
- âŠ