CAUSAL EFFECT ANALYTICS
ENHANCE YOUR DATA INSIGHTS AND
TAKE ADVANTAGE OF THE MOST REWARDING DATA ANALYSIS APPROACHES
Causal Effect Analytics is a data analysis approach that uses causal designs to analyse causal relationships, perform experiments, impact evaluations and to support evidence-based decision-making. The used tools and techniques belong to statistical-methodological field of Causal Inference – one of the most complex, but also the most rewarding data analysis approaches.
REVEAL SUBTLE DIFFERENCES AND ENHANCE DECISION-MAKING
The subtle differences can be revealed with data analysis approaches which use experimental frameworks to design studies and analyse data. Causal inference is based on the experimental framework, thus, the Causal Effect Analytics services enable you to squeeze-out of data the most valid and informative insights.
STATISTICAL METHODS BEHIND CAUSAL EFFECT ANALYTICS
We use a holistic approach to Causal Inference, which means that we combine the knowledge of causal designs with the knowledge of the analysed subject matter theory. Because we understand the importance of high quality data, we spend a significant amount of time developing the most appropriate causal design before proceeding with analysis of data.
A high quality data is a result of a well designed study and a carefully developed causal design.The “garbage-in, garbage-out” problem is overcome with a well-thought design. To avoid all sorts of biases, particularly when using behavioural data, a great deal of discussion is spent on eliminating biases. The use of statistical thinking is critical for this process as also understanding of cognitive biases.
WHAT CAN CAUSAL
EFFECT ANALYTICS DO FOR YOU?
- Do you deal with experiments, impact evaluations, personalizations, or target marketing?
- Do you want to enhance ML algorithms and improve accuracy of predictive analytics?
- Would you like to handle biases most effectively?
- Would you like to learn how to analyse causal relationships?
If the answer to at least one of the above questions is YES, Causal Effect Analytics will not only be of great help but the method of choice. Learn about the workflow of this service below.
WORKFLOW OF CAUSAL EFFECT ANALYTICS SERVICES
UNDERSTANDING DATA
We e-meet to discuss desired and possible data solutions by exploring available data sets.
DEVELOPING STRATEGY
We bring in the science of causal inference to prepare a strategy for study design and data selection.
PRESENTING DATA DESIGN
We e-meet again to discuss a suitable data design and a choice of data analyses that can be implemented.
IMPLEMENTING ANALYSIS
We assist your data analyses team with implementation of design and analysis, or we do it for you.
The number of e-meetings depends on data solutions we look at. The non-disclosure agreements are part of our culture.
P R I V A C Y F I R S T.
OTHER BENEFITS OF CAUSAL EFFECT ANALYTICS SERVICES
Working with us on your project enables your data team to learn about the methods and techniques that we use in Causal Effect Analytics. Your team learns about foundations to analyse causal relationships, perform experiments and impact evaluations. This service is designed to transfer the know-how of Causal Effect Analytics to our customers.
Schedule your first meeting here and learn on the go!
Interested in learning about Causal Effect Analytics tools and techniques?
TAKE ADVANTAGE OF THE MOST REWARDING ANALYSIS APPROACHES
LEARN ABOUT CAUSAL EFFECT ANALYTICS ONLINE
Join our online course on Causal Inference for Data Science and Analytics to learn how to analyse causal relationships in scientifically objective way. Some prior knowledge of statistics is recommended, however to those with little or no prior knowledge we recommend to start with our online course on Modern Statistical Thinking which equips you with key understandings on how to analyse data in scientifically objective way.
Join a learning adventure
that uplifts your understandings
about the world of data.
The importance of analysing causal relationships was recognised by Nobel Prize in Economics in 2021, awarded to Guido Imbens and Joshua Angrist for their research in the field of Causal Inference.