Noise has been found to enhance the emergence of interesting dynamical behaviors in some nonlinear physical systems. However, for any given system, a universal method for designing appropriate noise forms does not exist. This work proposes a theory-guided AI framework to design artificial noise capable of inducing energy-saving complete synchronization in any coupled nonlinear physical systems. This AI framework ensures that noise-induced synchronization is valid not only in the vicinity of the synchronization manifold but also far away from it. This achievement surpasses any traditional investigations. Consequently, this work opens broad avenues for regulating real physical systems using an appropriate amount of AI-designed noise.

Phys. Rev. E 110, L012203

Noise has been found to enhance the emergence of interesting dynamical behaviors in some nonlinear physical systems. However, for any given system, a universal method for designing appropriate noise forms does not exist. This work proposes a theory-guided AI framework to design artificial noise capable of inducing energy-saving complete synchronization in any coupled nonlinear physical systems. This AI framework ensures that noise-induced synchronization is valid not only in the vicinity of the synchronization manifold but also far away from it. This achievement surpasses any traditional investigations. Consequently, this work opens broad avenues for regulating real physical systems using an appropriate amount of AI-designed noise.

Has anyone used econml's CausalAnalysis object? Wanted to check if there are interpretation of results from that object

Hi, I have a dataset of around 20k customer which are exposed to various treatments(>1) and some customers which are not exposed but they are from past months. So the questions are :

1. How to go ahead with the methodology when every customer has been a part of Treatment?
2. How to go ahead when Control set is of the past dates as everyone is been a part of Treatment but customers in the past were not exposed to any Treatments?
3. How to evaluate the model results?

I am new to Causal Inference

Dear community, I'm new to the field of causal reasoning, and was wondering what conferences are there on the subject.

To give context:

• I'm a researcher in academia
• my field of research is (roughly) computer science and engineering -> artificial intelligence -> multiagent systems
• I'm especially interested in causal discovery (learning causal graphs from purely observation data and/or mixed observational + interventional data and/or online while doing interventions---alike reinforcement learning)
• I'm especially interested in applications to robotics, multi-agent systems, planning, reinforcement learning

Say, By one of various causal discovery methods, I try to find the causal graph for data of one hour, I need to update my causal graph for every hour. I need to rerun the algorithm again for the 2 hours of data so that I don't miss the relations from the previous hour. Are there any papers or update methods where there is no need for rerunning the algorithm and where only some of the coefficients or weights are updated?

Hey there, Causality Experts!

Do you have hands-on experience in the creation and application of causal diagrams and/or causal models? Are you passionate about data science and the power of graph-based causal models?

Then we have an exciting opportunity for you!

We - the HolmeS³-project - are conducting a survey as part of a Ph.D. research project located in Regensburg (Germany) aimed at developing a process framework for causal modeling.

But we can't do it alone - we need your help!

By sharing your valuable insights, you'll contribute to improving current practices in causal modeling across different domains of expertise.

You'll be part of an innovative and cutting-edge research initiative that will shape the future of data science.

Your input will be anonymized and confidential.

The survey should take no more than 25-30 minutes to complete.

No matter what level of experience or field of expertise you have, your participation in this study will make a real difference.

You'll be contributing to advancing the field and ultimately making better decisions based on causal relationships.

Click the link below to take our survey and share your insights with us.

https://lab.las3.de/limesurvey/index.php?r=survey/index&sid=494157&lang=en

We kindly ask that you complete the survey by May 2nd 2023 to ensure your valuable insights are included in our research.

Thank you for your support and participation!

Hi, looking for which unis in the uk have a strong research presence in causality, at the postgrad level.

I'm working with a large time-series dataset of smart building sensors (~3000). Is it possible to perform any kind of CD on this (most datasets only have N<100), and if I could recover a graph, how could I check it without knowing the ground-truth DAG?

Experts' intervention is required to create a causal graph. Is there any way we can create possible causal models using some automation? In some cases this can be useful.

Does anyone knows a good source which I can use to implement do-operator in Causality. It would be really helpful if someone shares some good link. Thank you in advance!

Hi redditors,

I'm new to the field of causality, in particular causal discovery (learning the structure, not the effects, of a causal graph, i.e. edges and their direction amongst variables).

I have a question about interventions that I intuitively answer, but cannot find a precise demonstration on papers (on the contrary, I found mentioning the opposite in a talk by a causal discovery expert)

Should multiple interventions be carried out mutually exclusively?

Assume the following setting (have faith :D):

• N > 1 agents have each partial knowledge of V variables in an environment
• some K variables out of V correspond to actuator devices that agents can operate
• agents need to perform interventions on some K to disambiguate the direction of some causal edges

Is it correct to say that, without any knowledge about the ground truth causal graph, the agents would need to intervene one at a time?

My intuition sees an intervention (within this context) as manipulating an actuator device all other conditions being equal, is this correct?

https://github.com/soelmicheletti/cdci-causality

I implemented a pipy package with a simple, yet effective, method to identify the causal direction between two variables. Check-it out!

It is a slightly modified version of the “Bivariate Causal Discovery via Conditional Independence” paper (https://openreview.net/forum?id=8X6cWIvY_2v). I’m working on an improved algorithm for binning, stay tuned for the new release!

A recent work published in RESEARCH on dynamical causality detection using time series:

https://doi.org/10.34133/2022/9870149 Continuity Scaling: A Rigorous Framework for Detecting and Quantifying Causality Accurately

If it is always the present moment, i.e. the Now, how can there be before and after? I cannot square this circle.

I am on the search for material on interactions between studies of time series and studies of causality. Interested in both directions of this link: finding causal influences in time series data but also to the more philosophical view that the time dimension us a big part of a causal relationship (the cause happens before the effect). For example, one can imagine that progress in machine learning can offer new tools to the field of causality. Reading "The book of why", I found a couple of mentions to time series which basically said that it's better to have controlled experiments rather than time series data which often hide spurious corrélations. I'd take that as a "pessimistic" view on this link, curious if someone else has talked about this subject, especially the temporal aspect of cause and effect

Is finding a Markov blanket/boundary a good way of creating a causal model? Basically finding a count of independent random variables that cause a dependent random variable to change?

https://en.wikipedia.org/wiki/Markov_blanket

Hi! Just found this sub and thought you all might enjoy this short play I wrote in 2012 called “Effect and Cause”. It runs in reverse, but the audience didn’t know. It was fun to watch the ripple of realization move through the crowd as the play progressed. I hope you like it…!

https://www.ineffable-solutions.com/_files/ugd/6f08db_5ae4f049fda44a1b9da6b0815cc8ef39.pdf

Apologies if the question is unclear, I'm not too familiar with causal inference.

I've been using a few different methods to estimate causal effects for an outcome variable through Microsoft's DoWhy library for Python. Despite using different methods (propensity backdoor matching, linear regression, etc.), the causal estimates are always very similar to a naïve estimate where I just take the difference in outcome means between the treated and untreated groups. I've used the DoWhy library to test my assumptions through a few methods of refuting the estimates (adding random confounders, removing a random data subset, etc.) and they all seem to work fine and verify my assumptions, but I'm still worried the estimates are wrong due to their similarity to the naïve estimates that don't take into account any possible confounding variables/selection biases.

Does this mean there's a problem with my causal estimates, or could the estimates still be fine? If there's a problem, is there any way to check whether it has something to do with my data (too high dimensionality), the DAG causal model I've created, or something else?