Facts vs Fiction
Distinguishing between facts and fiction is crucial for making informed decisions and protecting oneself from manipulation. Unfortunately, this task is becoming increasingly challenging in the face of the overwhelming surge of information, where misinformation, deceptive articles, and outright lies abound.
Fact-checking isn't always practical, as it can be time-consuming and often assumes that there is a definitive "correct" answer available.
To combat this growing problem, THE CAUSAL MINDSET offers a versatile tool that can be used almost anytime and anywhere to uncover flaws in arguments and enhance decision-making. Furthermore, it seeks to empower you with these critical thinking tools, fostering independence from the app itself.
Key Applications
How does this App works?
Share a statement, a graph, or a reflection with the app and it'll apply the Causal Mindset Framework to dissect and question the validity of the claim (see the examples in the next section).
Goal of the App
Enhance critical thinking and analytical skills, helping users become more immune to misinformation and better equipped to handle the complexities of their professional and personal lives.
Disclaimer
This App will not allow you to measure a causal effect but can guide you to understand limitations, what prevents you from measuring a causal effect in any situation and provide suggestions on the procedure to measure a causal effect in your case.
Initial Assessment
When a user presents a situation or a problem, the app first conducts a fundamental assessment of causality. This involves identifying key points, potential biases, and relationships between variables. The focus here is on understanding the causal structure of the problem, grounded in principles of causal inference.
Tailored Deep-Dive
Based on the initial analysis, the app then offers more detailed exploration tailored to the specific role or needs of the user:
Journalists/Content Creators: The app suggests further exploration into source verification and bias detection.
Professionals/Decision-Makers: For this group, the app provides advanced modules for risk assessment and decision analysis.
Students/Researchers: The app recommends educational resources on causal inference and opportunities for academic collaboration.
Examples snippet
Example 1: I followed a fitness program for rowing for a month. I tested today and went faster. Is it thanks to the program?
Answer: The Causal Mindset helps to reflect on the difference in motivation (placebo effect or motivational bias), and other potential confounded effects (e.g. diet change, another part of the training, sleep, etc.), and suggest ways to approach a causal analysis.
Read the full answer [here]
Example 2: In September 2022, Switzerland implemented an advertising campaign in favor of energy sobriety and observed a reduction in energy consumption after. Was it caused by the campaign?
Answer: The Causal Mindset notices the potential role of weather change, climate awareness, or energy price and suggests valid comparison to reduce and address these issues.
Read the full answer [here]
Example 3: I saw a company that provides free electric scooters in city centers across Europe and claims that their service reduces pollution. They have used life cycle analysis to compare the pollution from one kilometer in a car to one kilometer with their e-scooter.
Answer: The Causal Mindset highlights that free e-scooters might predominantly replace walking or public transport instead of car travel. Hence the net pollution reduction might be less than claimed or even negative.
Complete example
The Causal Mindset's answer:
Causal Mindset Application: We're using the causal mindset framework to analyze the apparent effect of the government's energy-saving campaign on net consumption.
Quick Feedback - Major Flaw: The graph shows a correlation between the start of the government's recommendations and a decrease in net consumption. However, a major flaw would be to immediately conclude causation. The decrease might correlate with other factors, such as seasonal changes in energy use.
Is there something else?: It's critical to consider alternative explanations for the observed decrease in net consumption. Seasonal temperature variations, economic activity, or even social changes due to the war in Ukraine may also influence energy consumption patterns.
Can We Extrapolate?: The external validity is questionable. The specific conditions in Switzerland during this period, like its economic structure, public receptiveness to government campaigns, and the social context of the war in Ukraine, may limit the generalizability of these results to other contexts or periods.
Counterfactual: We should consider what energy consumption might have looked like without the government's campaign. Would there still have been a decrease due to other factors such as increased individual awareness of energy issues?
Common Biases: Confirmation bias might lead us to attribute the decrease in consumption to the campaign because it aligns with our expectations. Seasonal effects, like warmer winters, could also confound the results.
Offering Solutions: To accurately measure the causality, one could propose a controlled study where similar regions are compared, with some not exposed to the campaign. Changes in consumption can then be more reliably attributed to the campaign itself.
For a deeper analysis, journalists could investigate the government's data collection methods and look for independent data sources. Decision-makers might use this data to inform future policy, while researchers could design studies to test the campaign's effectiveness more rigorously.
By considering these factors, we can avoid jumping to conclusions and instead appreciate the complexity of causality in this scenario.
Why Causality?
Understanding the concept of causality is crucial from fighting climate change, to our quest for happiness, including strategic decisions making.
Causality is interested about relating cause and effect and answering “why?” and “what if?”. And in essence the biggest questions today relate to this concept. For example:
What is driving climate change? What is the effect of this mitigation strategy?
What are the cause of the spread of a certain health issue? What if we encourage healthy life habits?
What is driving happiness? What if I reduce screen time?
Watch my TEDx explaining the concept and providing the basic tools:
About the author
I dedicated my career to bridging academia and civil society through innovative teaching and research. Beyond research, I taught statistics and causal inference to more than 10,000 people. over the last decade. More about myself on my personnal page.
The Framework behind the App
THE CAUSAL MINDSET is a set of practical tools inspired by statistics, and causal inference (the study of cause and effects), that I use to do scientific research, taught in academia and beyond.
I am currently finishing writting the book The Causal Mindset. You can sign up for the book updates here: Sign up
