More Information
Requirements:
The module will assume that participants are familiar with common research designs and have a good knowledge of general regression analysis. Some experience of Stata or any other syntax-based statistical software such as R or SAS would be helpful. The course is run through a blended model, with participants to be present on campus for two sessions a week over 5 weeks. Participants will need access to the internet and have a working version of Stata (version 14 or higher) installed on their laptop/computer to bring to the live sessions
Learning Outcomes: Subject specific: Knowledge, Understanding and Skills
On successful completion of this module the participant:
- will have acquired a thorough understanding of core concepts of causal inference such as potential outcomes, marginal and local causal effects, self-selection and confounding, non-compliance, effectiveness and efficacy, mediation;
- will have knowledge of population summaries which have a causal meaning and will be aware of research designs which allow estimating causal effects. This will help participants identify and articulate causal effects of interest in new research contexts and help them in planning empirical studies to assess them.
- will have obtained an overview of study designs and principled analysis methods (including Propensity Scoring, Structural Equation Modelling and Instrumental Variables methods) that can be used to provide valid estimates of causal effects.
- will have acquired the skill to implement popular causal analysis approaches using the Stata general purpose statistics software;
- will be able to apply this understanding in the interpretation of evaluation studies and the critical appraisal of research papers.
- will have practised the specification of causal research hypotheses and the running of relevant statistical analyses on a number of datasets from different contexts and so acquired practical experience as a causal analyst.
On successful completion of this module the participant:
- will be equipped with a range of statistical skills, including problem-solving, team work and presentation, which enable them to take prominent roles in a wide spectrum of employment and research;
- will be able to effectively communicate how causal modelling techniques can be applied to evaluate health treatments and risk factors to non-specialist audiences;
- will be able to critically assess their own work using discussion groups;
- will be able to show initiative and the ability to work autonomously and independently with minimal guidance from others;
- will be able to show confidence in the use of general purpose statistical software to implement causal modelling for real-life applications;
Refund Policy:
This policy shall apply to all courses run by the BHI department as part of the Executive Education Programme
You may cancel a course booking at any time. The standard BHI course cancellation policy (as specified below) shall apply.
• Cancellation more than three weeks before the event 100%
• Cancellation two weeks before event 50%
• Cancellation one week before event 0%
To cancel a course booking, you must let us know of your decision to cancel by emailing [email protected]. You must clearly state your intention to cancel the course. To meet the cancellation deadline, it is enough for you to send your communication concerning exercising the right to cancel before the cancellation period has expired.
We will make the reimbursement using the same means of payment as you used for the initial transaction. In any event, you will not incur any fees as a result of this reimbursement.
Whilst every effort is made to avoid changes to our programme, the BHI Department reserves the right to withdraw any course. If for any reason the BHI Department cancels a course, all course fees will be returned in full. We cannot however reimburse the cost of any pre-booked travel arrangements.
Limited Early Bird Discount of 10% if booked before 07th March 2024
Last Booking Date: Tuesday 30th April 2024
Time: 09:30 – 12:30 for the synchronous sessions with additional time spent watching the asynchronous videos in preparation for the next day.
Application: To apply please email [email protected] with the following details:
Email Subject Line: Application for Causal Modelling and Evaluation 2024
Name:
Email Address:
Contact Phone Number:
Indicate your education/employee status: KCL PhD, KCL student, KCL staff,
In 100 words, state your level of experience with regression models and statistical software.
In 100 words, state why you wish to enrol/participate in this course:
In 100 words, state which skills you hope to acquire:
Once your application has been approved, you will be sent a link to payment and a discount code if one is to be applied.
Please note the following:
Your place will not be confirmed until payment has been made.
Failure to cancel without sufficient notice will forfeit your course fee.
If you would like to pay by internal transfer, please contact [email protected]