CAUSALITY
Student’s name
Class
Date
1
Descriptive Inference
Descriptive inference refers to summarizing what is already known. It clarifies facts,
information, and data. Thus, it allows a simpler interpretation of the data.
Causal Inference
Causal inference considers study designs, assumptions, and estimation strategies to
explain relationships between the dependent and independent variables1. It helps identify the
change in the dependent variable if there are changes in the independent variable. As a
combination of logical arguments and statistical methods, causal inference allows researchers
to determine the actual impact of a certain phenomenon that is a component of a larger
system.
Increasing the Number of Observations
Lijphart acknowledges that increasing the number of observations helps draw a thin
line between comparative statistical methods and statistical methods2. However, researchers
should understand the methods such as code sheets and discussions of statistical computation
because science requires the best clarification for an observed phenomenon based on the
findings, and new information3. Thus, the methodology is important.
Lijphart’s ways
Firstly, one should use several pre-tests and post-test measures to decrease the
chances or probability. Secondly, it is important to use comparative methodology because it
plays a key role in concept-formation through bringing into focus contrasts and suggestive
comparisons. Comparison is primarily used in testing hypotheses and contributes to theory
building. Third, applying several areas, regions, or countries is crucial to decrease the risk of
invalidity-causing phenomena. Finally, one should present hypothetical notions for revision
and envisage the future significance of the case study.
Role of Causality
Causality is crucial as it helps link one procedure with another because the first
process may be responsible for the second4. The second one is dependent on the first.
Companies and organizations can use causal relationships. For instance, low wages may
cause less sales, illustrating the correlation between the two parameters. For researchers,
causal research helps them to take necessary actions to fix problems or optimize the desired
outcomes. Since people are motivated by their expectations of the future, the causes of
causality and teleology are crucial.
Rules for Constructing Causal Theories
The first rule is constructing falsifiable claims. Any theory should be designed to
illustrate wrongness easily. Secondly, develop consistent theories. At the heart of consistent
theories, individuals are motivated to seek coherent thoughts, values, beliefs, and behaviors
that align with their needs or desires. People also tend to seek consistency among cognitions
relevant to their attitudes.
Observable Implications and Causal Mechanism
1 Hernán, Miguel A., John Hsu, and Brian Healy. "A second chance to get causal inference right: a classification
of data science tasks." Chance 32, no. 1 (2019): 42-49.
2 King, Gary, Robert Keohane, and Sidney Verba. "Scientific inference in qualitative research." Princeton,
NJ (1994).
3 Bogue, Allan G. "Designing Social Inquiry: Scientific Inference in Qualitative Research." The Journal of
Interdisciplinary History 26, no. 4 (1996): 683-685.
4 Kay, Adrian, and Phillip Baker. "What can causal process tracing offer to policy studies? A review of
the literature." Policy Studies Journal 43, no. 1 (2015): 1-21.
2
A causal mechanism is the most immediate physical means or the procedure by which
accomplishments are made. On the other hand, observable implications aim to understand the
processes whereby the causes contribute to outcomes.
Identifying Causal Mechanisms
Social scientists identify causal mechanisms by permitting documentation of exact
causal mechanisms. Scientists develop a specific method to estimate the system by using
some estimations5. The single-experiment design where causal mediation is applied is popular
in various disciplines, including psychology.
Major Challenges Identified with Causal Mechanisms
The experiments fail to identify overall causal mechanisms. In some instances, the
value of the potential outcome can only be identified under one of the possible treatments,
especially if the parameters are not clearly defined. It is also challenging to explain the
procedure through which causal mechanisms occur.
5 King et al., “Scientific inference in qualitative research,” 53).
3
Bibliography
Bogue, Allan G. "Designing Social Inquiry: Scientific Inference in Qualitative
Research." The Journal of Interdisciplinary History 26, no. 4 (1996): 683-685.
Hernán, Miguel A., John Hsu, and Brian Healy. "A second chance to get causal inference
right: a classification of data science tasks." Chance 32, no. 1 (2019): 42-49.
Kay, Adrian, and Phillip Baker. "What can causal process tracing offer to policy studies? A
review of the literature." Policy Studies Journal 43, no. 1 (2015): 1-21.
King, Gary, Robert Keohane, and Sidney Verba. "Scientific inference in qualitative
research." Princeton, NJ (1994).
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